Abstract
Purpose of Review
Emerging adulthood (age 18–25) represents a critical period for weight control: rate of weight gain is greatest during these years and the prevalence of overweight and obesity is estimated to be at least 40% among emerging adults. Unique behavioral, psychosocial, and cognitive risk factors among this population must be specifically addressed within weight management programs. We review extant treatment approaches, including lessons learned from the nascent literature specifically targeting this population. Lastly, we provide suggestions to inform future work in this area.
Recent Findings
The EARLY consortium comprises seven clinical trials targeting weight control in young adults age 18–35. Though these studies encompass a broader age range, two of the trials enrolled large numbers of 18–25 year olds. Results from these trials and other recent pilot trials provide a foundation for next steps with respect to developing weight management interventions for emerging adults.
Summary
The design of targeted weight control approaches for emerging and young adults has contributed to improved outcomes for this high-risk population. However, suboptimal engagement and variability in response pose challenges. Identifying and intervening on individual-level behavioral and psychological variables may enhance the effects of these adapted treatments.
Keywords: emerging adulthood, weight management, overweight, obesity, intervention
Introduction
Weight gain during emerging adulthood (i.e., ages 18–25) is greater than any other period in the developmental life course. In fact, data indicate that the greatest gains occur in the early twenties and among those who are already overweight [1–2]. This is particularly concerning given the high rates of overweight and obesity that already exist among emerging adults, with general prevalence estimates over 40% and soaring above 50% among African-American and Hispanic/Latino populations [3]. Moreover, the looming cardiometabolic risks associated with weight gain and obesity in these years are well documented [4–8]. Compounding this problem is the relative lack of effective evidence-based weight control approaches for emerging adults. Weight-promoting developmental concerns and risk factors unique to this age group are not addressed in standard adult intervention approaches, perhaps contributing to their inadequacy for this population.
Defining Emerging Adulthood
The developmental period now known as emerging adulthood is a relatively recent addition to our conceptualization of the lifespan. Over the past several decades, shifts in Western societal expectations and norms have given rise to delays in marriage and parenthood, a prolonged educational process, and greater allowance for individual freedom and exploration—all of which serve to lengthen the period between adolescence and adulthood proper. While emerging adulthood encompasses this transition, it is associated with unique features that distinguish it as its own developmental period rather than an extended adolescence or a young adulthood [9–10]. This stage is defined by identity exploration, instability, self-focus, feeling in-between, and the perception of possibilities [11]. Though these processes of course follow individual timelines, emerging adulthood typically spans ages 18 through 25 if not beyond [9,11]. Importantly, while this conceptualization of emerging adulthood appears to be a phenomenon currently limited to industrialized nations, it is seen across educational and socio-economic strata therein [11]. It is also possible that this developmental experience will become more normative across cultures with increasing globalization over the coming years [12].
Socially, these years are associated with frequent transitions in the domains of work, school, family, and romantic relationships. The majority of emerging adults anticipate changing career paths at least once throughout their lifetime and expect to receive additional training or schooling at some point during mid-life [13]. Living situations vary widely and fluctuate frequently, as moving rates peak during this period [14]. Living situations of emerging adults are roughly evenly distributed across living at home with parents, living with husband/wife, living with friends/roommates, living alone, and living with a boyfriend/girlfriend [15]. Though only 30% live with their family of origin, more than half are in daily contact with their parents, and 60% receive at least occasional financial support from parents [15]. With respect to romantic relationships, emerging adults place high value on finding a long-term partner [15–16], though few remain in the same relationship throughout the ages of 18–25 [17].
While its hallmark characteristics are psychosocial in nature, biology may also play a role in the behavioral patterns exhibited throughout emerging adulthood. Advances in neuroscience imaging have furthered our understanding of brain development across the lifespan. Evidence suggests that post-adolescent frontal lobe maturation continues into the third decade of life [18–19], which may correspond to neuropsychological development in associated areas of executive functioning including organization/planning, response inhibition, emotion regulation, goal-directed behavior, and metacognition [19–21]. However, the extent to which these physiological changes map onto real-world behavior remains unknown [22].
Given this context, it is not entirely surprising that it has proven to be particularly challenging to recruit, engage, and retain emerging adults in programs that promote self-regulation of health behaviors. This is perhaps most evident within the context of weight gain prevention and weight loss interventions, which require multiple health behavior changes reliant upon collecting, evaluating, and making adjustments in accordance with personal data in the form of weight, dietary intake, physical activity, and other goal attainment. In order to design effective programs for emerging adults, it is necessary to account for the considerations unique to this developmental period rather than simply applying standard adult programs to this population. Calls for weight control approaches targeting young adults [23–24] represent a shift towards acknowledging this point; however, fall short of recognizing the distinction between emerging adulthood and young adulthood—the critical distinction between which is the “figuring out” of adulthood versus the early establishment of adulthood [11]. In fact, in asking emerging adults to determine priorities and accept individual responsibility in the context of weight control programs, we are guiding them toward bridging this gap.
The Risks
Behavioral Risk Factors
Eating
The transition into early adulthood is associated with myriad unhealthy eating behaviors. Evidence suggests that very few emerging adults are consuming the recommended fruit and vegetable amounts [25]. Further, data point to increases in fast food consumption [26–27] and intake of sugared beverages [28], as well as increased alcohol and binge drinking during these years [29]. Moreover, beverage and fast food companies have successfully created campaigns that specifically target emerging adults through price reduction, utilizing known athletes, promoting late night meals, and expanding beverage selections to include various sports drinks [30].
Physical activity
The majority of emerging adults are not meeting the recommended national physical activity requirements [31]. Physical activity declines during the transition from adolescence into emerging adulthood, and this drop continues as emerging adults reach young adulthood [32]. Of note, data indicate that emerging adults are interested in exercise and this might be a point of entry for weight control interventions [33], yet findings also suggest this population experiences barriers to physical activity that might be exacerbated by this transitional developmental period, including lack of time [33–35], motivation [34], and social support [34].
Routine and sleep
While average sleep duration increases throughout the transition from adolescence into adulthood, nearly 9% of emerging adults report less than 6 hours of sleep per night [36]. Inconsistent routines due to holding multiple jobs [37], working while attending school [15], and nontraditional work schedules [38] contribute to poor sleep quality and short sleep duration in this age group [39]. Though much remains to be discovered regarding the relation between sleep and weight, extant evidence supports a significant link between inadequate sleep and poor weight-related and cardiometabolic outcomes [40–42]. The link between insufficient sleep and obesogenic behavior is thought to be mediated by disruption of appetite-regulating hormones, resulting in excess caloric intake [43–44]. Of note, the association between inadequate sleep and BMI/waist circumference appears to be stronger among emerging and young adults compared to other age groups [45].
Psychosocial and Cognitive Risk Factors
Stress and emotions
Compared to other adults, emerging adults find this time in their lives to be stressful [15], and are more likely to manage this stress by engaging in unhealthy weight-promoting behaviors such as eating and drinking alcohol [46]. They report often feeling anxious and depressed [15], and one out of five emerging adults will meet clinical criteria for a depressive episode during these years [47]—the highest prevalence of any age group [48]. To make matters worse, emerging adults are unlikely to seek [47] and receive [47,49] adequate treatment for depression. While periods of acute stress are typically associated with appetite suppression and potential weight loss [50], there is some evidence to suggest that chronic stress promotes weight gain [51]. Importantly, emerging adults specifically identify stress as an interrupter of healthy habits and a barrier to successful weight loss attempts [33].
Physical and interpersonal environment
Maintaining a healthy weight can be challenging when residing in an obesogenic [52] environment (i.e. fast food restaurants, limited fresh food options); the natural response is to indulge in high-calorie food [53]. While the U.S. as a whole arguably is exposed to this toxic environment [54–55], emerging adults face unique challenges in terms of their physical environment, including leaving their family of origin for the first time, transitions into secondary education and/or workforce, living on their own and navigating meal preparation and the cost of healthful eating [14,56–57]. Further, 20.5 million emerging adults attend American colleges and universities [58], and thus are exposed to amenities (i.e. all- you-can-eat buffets) and a new lifestyle (i.e. snacking late at night, increased alcohol consumption) that is perceived to influence weight-related behaviors [34–35]. Moreover, college meal plans and dining options increase access for unhealthy food consumption and overeating [35]. In addition to the physical environment, evidence suggests the social environment may play a critical role during these years. Data indicate emerging adults with overweight and obesity have more overweight romantic partners, friends, and social contacts relative to their non-overweight peers [59]. Furthermore, among individuals seeking obesity treatment, being part of a social network where eating healthy is not the norm and having overweight friends is associated with poorer weight loss outcomes [60].
Executive functioning
Though it remains unknown to what extent continued normative development of the prefrontal cortex influences health behaviors among emerging adults [22], individual differences in executive functioning have been linked to weight status such that obesity is associated with poorer planning and problem-solving abilities [61]. Among young adults in particular, strengths in task initiation and inhibitory control have been associated with intake of fruits/vegetables and avoidance of high-fat foods, respectively [62]. To date, most research in this area has been correlational; thus, the directionality of these relationships cannot be determined. Given that executive functioning has been proposed to underlie self-regulation [63]—a critical process in achieving and sustaining successful weight loss [64–65]—this domain warrants additional research as it relates to weight management efforts in this population.
Weight Loss Treatment for Emerging Adults
The challenge of overweight and obesity in emerging adulthood is compounded by the fact that traditional methods for addressing weight loss appear to be insufficient in meeting the needs of this population. Other adults might choose to discuss weight management with their primary care physician, join a commercial weight loss program, or participate in a behavioral weight loss trial; however, these avenues seem to be either unappealing, inaccessible, or unknown to emerging adults, given their paltry representation in such settings.
Historically, emerging adults have been less likely to maintain health insurance and engage in preventive care services compared to other age groups [66–68]. While the 2010 Affordable Care Act has successfully improved access to health care in some respects [66], significant barriers remain for this high-risk population. While those ages 18–26 are now insured at higher rates, they find it difficult to navigate the transition from pediatric to adult health care systems [69]. Further, despite an array of recommendations developed and implemented in medical specialty clinics, no standardized guidelines exist specifically for preventive care targeting emerging adults [70]. Even if emerging adults were to present in preventive care settings at rates comparable to other age groups, it is unlikely that they would receive effective weight management counseling given that that evidence-based counseling occurs at alarmingly low rates within the primary care setting. In fact, previous reports indicate that weight loss recommendations in accordance with NHLBI standards is low for the general patient population [71], and weight reduction advice occurs at less than 4% of primary care visits with emerging adults [69]. Interestingly, while emerging adults represented only 8% of patients enrolled in a multi-site primary care survey study, they were also the most willing to participate in a comprehensive weight loss program [72]. However, some evidence suggests that such programs may not be as effective for young adults compared to middle-aged adults [73]. Furthermore, emerging adults generally are underrepresented in studies examining the effects of primary care-based weight management programs, where mean participant ages range from 40–55 [74–75].
A similar pattern is seen with respect to commercially available weight loss approaches such as Weight Watchers, Jenny Craig, and NutriSystem among others. These programs are generally marketed toward middle aged or older adults, as evidenced by program spokespeople (e.g., Oprah Winfrey, Kirsti Alley, Marie Osmond) and specialized plans designed for members aged 60+. The mean age of customers in such programs hovers around 40 [76–77], with data suggesting that early dropout is associated with younger age [78]. Young adults who have lost weight successfully were significantly less likely than other adults to have done so with the assistance of a commercial program [79]. While commercial programs developed over the past decade (e.g., 21 Day Fix, Shakeology, Insanity) appear to target a younger demographic, little is known about the effectiveness of such approaches for weight loss.
Behavioral weight loss clinical trials also suffer from a dearth of emerging adult enrollees, thus limiting the generalizability of findings to this population [80]. Further, extant data indicate that young adult participants in these studies fare worse than their older adult counterparts with respect to weight loss outcomes, engagement, and retention [80]. An awareness of the need for targeted approaches to weight control in young adults has been growing over the past decade [24–25, 80], leading NIH to fund seven randomized controlled trials testing weight management approaches among participants age 18–35: the EARLY Trials (Early Adult Reduction of weight through LifestYle intervention; www.earlytrials.org). However, even among these targeted studies, mean/median ages ranged between 27.5 and 30.9 for all but two trials (Table 1) and none of the trials targeted emerging adults 18–25 years specifically; further, other participant characteristics of these samples (e.g., college students, majority non-Hispanic white) limit generalizability of findings to emerging adults more broadly (Table 1). Moreover, it is unclear whether differing trial goals – i.e., weight gain prevention versus weight loss – differentially attract emerging versus young adults.
Table 1.
Trial | Sample Characteristics | Intervention | Main Outcomes |
---|---|---|---|
aChoosing Healthy Options in College Environments & Settings (CHOICES)87,90,98–100 | N=441 67.6% female 72.6% white 100% student (2-year college) Age= 22.7 (5.0)b BMI = 25.4 (3.8)b |
24-month weight gain prevention intervention Intervention: 1-credit college course + intervention website with social support, resources, and self-monitoring platform Control: Quarterly health promotion information |
No difference in BMI, weight, waist circumference, or body fat percentage between intervention and control at 24 months Significant reduction in prevalence of BMI ≥ 25 in intervention compared to control at 24 months |
Cell Phone Intervention for You (CITY)84,96 | N=365 69.6% female 56.2% white 34.3% student Age = 29.4 (4.3)b BMI = 35.2 (7.8)b |
24-month weight loss intervention Intervention 1 (CP): Comprehensive interactive cell phone app used for both intervention delivery and self-monitoring Intervention 2 (PC): Delivered via group sessions and personal coaching; self-monitoring via smartphone Control: Provided 3 handouts on healthy eating and physical activity |
No difference in weight loss between groups at 24 months Significantly greater weight losses in PC compared to both CP and Control at 6 and 12 months |
eMoms83 | N=1689 (ITT) 100% pregnant women 68.0% white [student status not reported] Age = 27.5 (4.7)b BMI = 25.4 (4.3)b |
See footnoted | See footnoted |
Innovative Approaches to Diet, Exercise, and Activity (IDEA)82,97 | N=471 71.1% female 77.2% white 25.7% student Age = 30.9 (27.8–33.7)c BMI = 31.2 (28.4–34.3)c |
24-month weight loss intervention Intervention 1 (Standard): in-person group behavioral weight loss + phone counseling/study website; self-monitoring via website Intervention 2 (Enhanced): in-person group behavioral weight loss + phone counseling/study website; self-monitoring via provided wearable device + web platform |
No difference in body composition, physical activity, or dietary behaviors between groups Significantly greater weight losses in Standard compared to Enhanced at 12, 18, & 24 months |
Study of Novel Approaches to Weight Gain Prevention (SNAP)85,101–102 | N=599 78.3% female 73.1% white 24.3% student (full-time) Age = 28.2 (4.4)b 26.2% age 18–24.9 BMI = 25.4 (2.6)b |
4-month weight gain prevention intervention with low-intensity follow-up (average 3 years) Intervention 1 (SC): 10 group in-person sessions followed by online weight reporting with feedback and quarterly online refreshers; instructed to make small, daily changes to eating and activity Intervention 2 (LC): 10 group in-person sessions followed by online weight reporting with feedback and quarterly online refreshers; instructed to make large changes initially to produce weight loss of 5–10lbs to create buffer against future gains Control: 1 group in-person meeting providing an overview of both SC and LC approaches |
Significantly greater weight losses in both LC and SC compared to Control at primary endpoint (average follow up of 3 years); significantly greater weight losses in LC compared to SC at primary endpoint |
aSocial Mobile Approaches to Reduce Weight (Project SMART)86,91,93–94 | N=404 70.3% female 41.8% white 100% student Age = 22.7 (3.8)b BMI = 29.0 (2.8)b |
24-month weight loss intervention Intervention: Delivery across multiple channels: Facebook, mobile apps, website, email, text, & health coaching Control: Access to general health education website without social networking components |
No difference in weight loss between intervention and control at 24 months Significantly greater weight loss in intervention at 6 and 12 months compared to control |
Treating Adults at Risk for Weight Gain with Interactive Technology (TARGIT)81 | N=330 48.8% female 57.3% white [student status not reported] 100% current smokers Age = 29.7 (4.2)b BMI < 25: 23.0% 25–29.9: 37.6% 30+: 39.4% |
See footnoted | See footnoted |
Mean age of enrolled participants within emerging adult range.
Mean (standard deviation)
Median (25th–75th percentile)
Full study description and outcomes not yet available for these trials. Additionally, both trials concerned weight control in specific populations: pregnant women (eMoms) and current smokers (TARGIT); therefore, results may not be applicable to weight management more generally in a young adult population.
Thus, despite demonstrating clear and unique weight-related risks throughout the years of 18 to 25, emerging adults have minimal opportunities for weight control treatment and support. It is no surprise that they are more likely than other adults to attempt weight loss on their own—perhaps due in part to this lack of options [79]. At the same time, evidence suggests they would be interested in participating in a weight loss program if it were well-suited to their needs, highlighting a critical area for continued research and development [33]. The large-scale efforts of the EARLY consortium, in combination with other formative work and smaller randomized pilot trials, provide a foundation from which to draw for future endeavors to recruit and engage an emerging adult population in weight management programs.
Lessons Learned
Recruitment
Formative data conducted with young and emerging adults indicate a general need to adapt standard recruitment methods in order to reach this otherwise difficult to reach [88] population. With respect to messaging, emerging adults expressed that an emphasis on the broad benefits of lifestyle change (e.g., improved energy, fitness, and health) is more appealing than a narrow focus on “weight loss” [33,84]. Young adults also recommended highlighting specific aspects of trials when advertising, such as incentives and convenience [84]. Trial branding including study name, logo, colors, taglines, and images has also proven to be an effective strategy for recruiting emerging and young adults into weight management trials [83,85,89]. Images in particular have the potential to either sink an ad or make it hit home for emerging adults, and should depict people with relatable body types engaging in healthy behaviors to appeal to this age group [33].
Recruitment avenues proposed by young and emerging adults include Internet-based outlets such as social media, website ads, and email blasts in addition to more traditional methods such as print flyers and in-person tabling events at colleges and community events [84,90]. The ability to learn more about the study and complete the screening process online rather than via phone or in-person was especially salient in focus groups with emerging adults [33], though this strategy was implemented by a minority of EARLY trials—of note, not only are other screening methods associated with higher staff burden [87], but failure to utilize online recruitment and screening tools might have contributed to lower enrollment of 18–25 year olds in many of these trials. Underscoring this point, the TARGIT trial found that participants who responded to technology-based advertisements were on average 3 years younger than those who heard about the study through other methods [81]. In our own work with emerging adults, a very small minority of potential participants choose to complete the screening process via phone or in person, instead opting for the convenience of an online questionnaire.
However, much remains to be investigated about the ways in which emerging adults engage in technology-based advertising for weight management programs. Advertisements posted through “organic” methods (i.e., an established online profile with social capital) may yield different results compared to those purchased and appearing as sponsored ads. Further, popularity of specific platforms within the 18-to-25-year-old demographic changes rapidly, necessitating a flexible recruitment strategy focused less on particular domains and more on overall approach. Nuances such as these may partially explain the unexpected finding that Facebook advertising did not produce a significant return on investment in the CITY trial despite formative work emphasizing the importance of advertising on social media [84].
An analysis of the recruitment methods implemented in the CITY [84] and SNAP [85] trials highlight mass mailings as an effective method for reaching young adults as a whole; however, mass mailings were found to be significantly less effective for enrolling emerging adults 18–25 years compared to young adults age 26–35 [85]. This finding makes sense in the context of emerging adults moving frequently between living situations [14]. Instead, SNAP investigators found that relatively low-cost methods such as print flyers, word of mouth, and posting to free email listservs appeared to be popular among the youngest participants; CHOICES investigators recruiting on college campuses reported similar success with email blasts and information tables [87].
Project SMART investigators propose the use of marketing strategy when recruiting for clinical trials, explicitly conceptualizing recruitment as a buyer decision process wherein potential participants recognize a problem, search for information, and evaluate alternatives [91]. Further, they endorse the simultaneous use of multimedia outlets in order to create a media blitz, thus raising awareness and building brand recognition. This approach contributed to their success in on-time and within-budget recruitment for their EARLY trial targeting weight loss among college students [91]. We have used a similar approach successfully in our previous pilot trials with emerging adults [89, 92] as well as our ongoing fully-powered weight loss trial (REACH; www.reachtrialrva.com) targeting 18- to 25-year-olds, which has thus far enrolled 181 participants—diverse with respect to race/ethnicity as well as student status. In this endeavor, we have found social media, broadcast radio, and online radio (e.g., Pandora) to provide the greatest yield of eligible participants. Echoing the call put forth by Gupta and colleagues [91], we have found it crucial to continuously evaluate the market and develop a recruitment strategy capable of responsiveness to trends within the target population.
Program Components and Structure
Focus on overcoming unique challenges
Formative work conducted with emerging adults has yielded a consensus that weight management programs must provide solutions for the challenges associated with this developmental period in order to be appealing and effective. Juggling responsibilities between school, work, family, and social life leaves little time for the gym, and makes high-calorie convenience foods more attractive—or conversely, perhaps contributes to skipping meals [33,90]. Stress has also been identified as a significant barrier to maintaining motivation for healthy lifestyle change, sparking an “unraveling” of any progress made [33, p.351]. Moreover, programs designed for emerging adults must go beyond current standard behavioral weight loss to provide content specifically targeting time and stress management in addition to other key areas identified in formative work such as alcohol use, sugar-sweetened beverage consumption, environmental barriers, and sleep [33,90].
Role of technology
In addition to the previously noted importance of technology in the recruitment and screening process, emerging adults also expressed interest in a weight management program involving technology, though importantly, perhaps not solely reliant upon technology. Limiting intervention delivery exclusively to technology-based modalities has the potential to reduce perceived accountability and accelerate disengagement over time for young adults [93]. These factors may hamper the impact of such programs, as evidenced by modest and attenuated intervention effects reported by Project SMART, which was delivered via integration across social media, text messaging, mobile apps, and a website [94].
Findings of formative work conducted with emerging adults reflect a split preference between a technology delivered program and an in-person program [33], with a suggestion for combined delivery modality [90]. This hybrid approach seems to provide a balance between emerging adults’ preference for a flexible, convenient program and their desire for minimal face-to-face interaction with an interactive or experiential focus [33,90]. Blending a technology platform with in-person activities has demonstrated promise in emerging adult samples [89,95]; a randomized pilot trial targeting 18–25 year olds specifically conducted a direct comparison of technology-only (i.e., web-based platform), in-person-only, and hybrid delivery of behavioral weight loss found that the hybrid approach was most successful in promoting engagement and yielding clinically significant weight loss outcomes [89].
Similarly, there is some evidence from the EARLY trials to suggest that a hybrid approach might hold promise for this population in the short-term, although sustained effects remain elusive. The CITY trial found a hybrid approach comprising in-person group classes and individual coaching phone calls supplemented with smartphone-based self-monitoring to be more effective than control at 6-months [96], whereas the smartphone based intervention alone was not significantly different from control at 6-months. Of note, effects dissipated by longer-term follow up, highlighting the continued challenges of sustained engagement and non-use attrition in technology based trials, as well as the multi-faceted challenge of weight loss maintenance. Similarly, the IDEA trial found that their hybrid intervention delivery (6 months of group in-person treatment followed by use of intervention website, text messages, and brief monthly coaching phone calls) was more effective on its own than with the addition of a wearable physical activity monitor during the maintenance phase [97].
The CHOICES trial delivered a weight gain prevention trial across two phases: a one-credit college course followed by an intervention website offering social support, additional materials, and a self-monitoring platform [98]. Participants were able to choose between taking the course solely online or having some degree of face-to-face contact, with a relatively even split between these preferences. While there were no overall intervention effects on BMI compared to control, the prevalence of overweight/obesity was significantly reduced in the intervention condition [99]. Further, retention was highest among participants who chose the face-to-face course [100]; however, because delivery modality was not randomized, it is unclear whether this finding is due to individual-level characteristics.
Lastly, the SNAP trial compared two self-regulation approaches to weight gain prevention (large changes and small changes) and a self-guided control group. The intervention groups received 10 in-person sessions (8 weekly group sessions followed by 2 monthly group sessions), after which participants reported their weights online and received monthly automated feedback via email [101]. In addition, the program offered annual 4-week refresher courses, primarily delivered via the Internet. Impressively, both active interventions in SNAP successfully prevented weight regain compared to control at a mean follow-up of 3 years post-baseline [102].
Role of autonomy
Developmentally, emerging adulthood is a time of increasing autonomy, identity exploration, and self-focus [11]. At the same time, this period is also marked by feeling in-between [11], which may represent an uncertainty regarding the extent to which emerging adults want to commit to their autonomy. While desire for autonomy and choice emerged as a central theme in our formative work with emerging adults [33], they have also identified individual choice as a potential barrier due to a tendency to be influenced by peer pressure or environmental factors [90]. This apparent tension between a need for both autonomy and accountability may be resolved by promoting choice within the context of an autonomy supportive coaching relationship. We piloted this approach in a randomized trial compared to standard delivery and found that an autonomy supportive approach facilitated engagement and retention in a weight loss trial for emerging adults [92]; however, it will be important to replicate these results within the context of a fully-powered trial.
Related, emerging adults’ desire for autonomy is also seemingly at odds with their enthusiastic support of financial incentives within the context of a weight management program [33,90]. However, recent data suggest that a compromise may be possible between these two approaches: an incentive paradigm involving small payouts linked to discrete behaviors (i.e., self-monitoring of weight and energy intake) has been shown to enhance program engagement and weight loss outcomes without undermining intrinsic motivation [103]. In our ongoing 3-arm randomized controlled weight loss trial with emerging adults (REACH), we are testing the effects of this incentive paradigm within the context of a behavioral economics arm compared to both a self-determination theory based arm and a standard behavioral weight loss arm in order to assess effects on intrinsic/extrinsic motivation in addition to weight and cardiometabolic outcomes. The delivery mode across all arms is an identical hybrid approach consisting of minimal in-person contact (one in-person group session and one in-person individual session) followed by a technology-mediated platform.
Self-monitoring
Within the context of behavioral weight management programs, self-monitoring of is imperative for success [64–65,104]. Trials targeting emerging adults are no exception, where self-monitoring of weight, calorie intake, and physical activity are associated with better weight loss outcomes [89]. However, teaching the concept of self-monitoring to emerging and young adult populations carries unique concerns and considerations. The first is the question of whether self-monitoring of these behaviors could promote unintended consequences such as depression and disordered eating due to increased emphasis on areas of dissatisfaction. However, available evidence suggests that self-monitoring within the context of weight management trials does not lead to deleterious effects. Among young adult participants enrolled in the SNAP trial, frequent self-weighing was not associated with unhealthy weight control practices [105]. In our own work, we have demonstrated the feasibility of encouraging 18-to-25-year-olds to increase their self-weighing frequency over the course of a 3-month lifestyle intervention [106]; this increase was associated with greater weight loss success at post-treatment. Further, frequent self-weighing—that is, weighing 4 or more days per week—was associated with achievement of clinically significant weight loss at post-treatment.
The second issue is how to best to promote engagement in self-monitoring among emerging and young adults, as this practice is not inherently rewarding and can be viewed as a chore. One potential solution is incorporating technology to decrease demands on effort and time. The idea of “the quantified self” [69] is not unfamiliar with the advent of wearable technology such as the FitBit and online dietary self-monitoring apps such as LoseIt and MyFitnessPal. Despite its increasing presence in our zeitgeist, it is unclear whether emerging adults in particular are engaging in this trend of tracking health behaviors. In our weight loss trials with emerging adults, we highly encourage the use of such apps, but still see significant declines in self-monitoring behaviors over the course of the intervention. Another avenue of interest is enhancing motivation specifically for self-monitoring, which is one aim of our ongoing REACH trial.
Lastly, even if engagement in self-monitoring were to remain high across the course of a trial, the extent to which emerging and young adults actively use these data to inform decisions about their own weight-related behaviors is unclear. The IDEA trial found that the addition of wearable technology did not result in weight loss maintenance above and beyond a standard behavioral weight loss intervention for young adults [97], underscoring the fact that self-monitoring alone is only half the story. Fostering a framework of self-regulation wherein emerging and young adults are able to leverage self-monitoring data to make informed decisions about food choice and exercise on a day-to-day basis is crucial for long-term success. Additional work is needed in order to develop best practices for expressing the importance of these behaviors within the context of a self-regulation framework to promote self-efficacy in this population. While technology-aided self-monitoring may be appealing to emerging and young adults due to reduced burden, we must take care not to promote these tools as a way to eliminate cognitive effort. This could prove to be a difficult balance to strike, and use of these tools to deliver programs might require innovative methods for teaching self-regulatory concepts and enhancing participant engagement.
Conclusion and Future Directions
Secondary analyses of emerging adults’ performance in the EARLY Trials will provide insight into key differences between those age 18–25 and those age 26–35, even within the context of adapted programs. For example, the CITY trial found that their youngest participants lost more weight than did their oldest participants. Despite a lack of treatment effects for their smart-phone based intervention and a lack of sustained treatment effects for their hybrid approach relative to control, the possibility that both treatments were more successful with younger participants is an exciting finding. However, due to overall low emerging adult enrollment, it may be difficult to conduct further analyses regarding predictors of success within this age group.
As it stands, there exist several challenges that must be reckoned with in order to bolster the effectiveness of lifestyle interventions for emerging adults. While some studies have demonstrated success in producing clinically meaningful weight loss among those 18–25, variability in treatment response is broad, reflecting a wide range of outcomes [89,107]. Identifying variables associated with success—related to both individual characteristics (e.g., depression/stress, executive functioning, race/ethnicity) and process (e.g., early weight losses, engagement)—will be a crucial endeavor in coming years.
Fostering sustained motivation and engagement is another concern, as we know that this is linked to treatment success, but tends to be relatively elusive in emerging adult samples. In their CHOICES trial, investigators reported declines in online engagement as soon as 4 weeks post-treatment launch; course module completion rarely surpassed 90%, and face-to-face attendance tended to be below 80% [99]. In Project SMART, the proportion of highly engaged participants drastically declined over the first 3 months of the trial, and overall engagement declined at a relatively steady rate across 24 months [91]. Additional research is needed in order to determine the best ways to promote re-engagement with lifestyle interventions, such as coach reach-outs, specialized content, provision of resources, etc. Deploying effective strategies within the context of adaptive interventions may help to reduce overall variability in treatment response and enhance effects for emerging adults.
A relative lack of racial/ethnic minority participants limits generalizability of the findings of many of these trials. Even among studies with strong initial minority recruitment, differential loss of these participants throughout the screening process remains an issue [81,87]. In addition, results from the IDEA trial reflected significantly poorer weight loss outcomes for non-white participants [106]. This pattern across trials is especially concerning given that racial/ethnic minorities are at greater risk for obesity and related health consequences [3,108], and underscores the importance of extending the reach of future interventions to meet the needs of those emerging adults at highest risk.
One theme that does appear to be clear is a caution against the overreliance on technology for emerging adults. Due to the constantly evolving tech landscape, investing in specific approaches may limit generalizability or become a confounding variable in multi-year trials. Social media approaches especially may fall prey to ebbs and flows in popularity and acceptability among emerging adults. Even within automated approaches to intervention delivery, a human touch may improve acceptability and engagement with this population. Based on the work conducted to date, it appears as though delivering weight management interventions solely through technological modalities is insufficient to effect significant change in weight among emerging and young adults. At the same time, increasing our investment in the avenues whereby technology has proven to be particularly helpful—i.e., recruitment websites, online screening, long-term reporting of key behaviors—will allow for greater success in these areas.
Continued work with this population has the potential for immense public health impact—not only with respect to curtailing the trajectory of weight gain and obesity on an individual level, but also in terms of decreased medical costs and halting generational transmission of obesity. Further, fostering a wide range of health habits within the context of a lifestyle intervention (i.e., dietary quality, physical activity, stress management, sleep quality) has the potential to improve not only weight and cardiometabolic outcomes, but also psychosocial outcomes such as depression and perceived stress. Emerging adults are at a prime time in their lives to establish the habits that they will implement as they continue to age and as life circumstances become more stable; if they can achieve success amidst the chaos associated with the transition to adulthood, this may bode well for continued success during future stressors. Further, accomplishing goals with respect to weight management may promote the sense of individual responsibility necessary to successfully navigate the transition from emerging adult to young adult.
Footnotes
Compliance with Ethical Standards
Conflict of Interest
Autumn Lanoye, Kristal L. Brown, Jessica G. LaRose declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article is a review of previously conducted research so did not involve human subjects. Reviewed studies performed by the authors were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.
References
Papers of particular interest, published recently, have been highlighted as:
• Of importance
•• Of major importance
- 1.Williamson DF, Kahn HS, Remington PL, Anda RF. The 10-year incidence of overweight and major weight gain in US adults. Arch Intern Med. 1990;150:665–672. doi: 10.1001/archinte.1990.00390150135026. [DOI] [PubMed] [Google Scholar]
- 2.Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The spread of the obesity epidemic in the United States, 1991–1998. JAMA. 1999;282:1519–1522. doi: 10.1001/jama.282.16.151. [DOI] [PubMed] [Google Scholar]
- 3.Muyle TP, Park MJ, Nelson CD, Adams SH, Irwin CE, Jr, Brindis CD. Trends in adolescent and young adult health in the United States. J Adolesc Health. 2009;45(1):8–24. doi: 10.1016/j.jadohealth.2009.03.013. [DOI] [PubMed] [Google Scholar]
- 4•.Truesdale KP, Stevens J, Lewis CE, Schreiner PJ, Loria CM, Cai J. Changes in risk factors for cardiovascular disease by baseline weight status in young adults who maintain or gain weight over 15 years: the CARDIA study. Int J Obes. 2016;30:1397–1407. doi: 10.1038/sj.ijo.0803307. Findings of this study underscore the importance of weight control in young adulthood: weight gained specifically during these years was associated with poorer cardiometabolic functioning 15 years later, regardless of initial weight status. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Norman JE, Bild D, Lewis CE, Liu K, West DS, CARDIA Study The impact of weight change on cardiovascular disease risk factors in young black and white adults: The CARDIA study. Int J Obes Relat Metab Disord. 2003;27:369–376. doi: 10.1038/sj.ijo.0802243. [DOI] [PubMed] [Google Scholar]
- 6.Carnethon MR, Loria CM, Hill JO, Sidney S, Savage PJ, Liu K. Risk factors for the metabolic syndrome: the Coronary Artery Risk Development in Young Adults (CARDIA) study, 1985–2001. Diabetes Care. 2004;27:2707–2715. doi: 10.2337/diacare.27.11.2707. [DOI] [PubMed] [Google Scholar]
- 7.Lloyd-Jones DM, Liu K, Colangelo LA, Yan LL, Klein L, Loria CM, et al. Consistently stable or decreased body mass index in young adulthood and longitudinal changes in metabolic syndrome components: the Coronary Artery Risk Development in Young Adults Study. Circulation. 2007;115:1004–1011. doi: 10.1161/circulationaha.106.648642. [DOI] [PubMed] [Google Scholar]
- 8.Merten MJ. Weight status continuity and change from adolescence to young adulthood: examining disease and health risk conditions. Obesity (Silver Spring) 2010;18:1423–1428. doi: 10.1038/oby.2009.365. [DOI] [PubMed] [Google Scholar]
- 9.Arnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. Am Psychol. 2000;55:469–480. doi: 10.1037/0003-066X.55.5.469. [DOI] [PubMed] [Google Scholar]
- 10.Roisman GI, Masten AS, Coatsworth JD, Tellegan A. Salient and emerging developmental tasks in the transition to adulthood. Child Dev. 2004;75:123–133. doi: 10.1093/acprof:oso/9780199736546.003.0. [DOI] [PubMed] [Google Scholar]
- 11.Arnett JJ. Emerging adulthood: The winding road from the late teens through the twenties. New York: Oxford University Press; 2004. [Google Scholar]
- 12.Arnett JJ. Adolescence and emerging adults: A cultural approach. 5th. Upper Saddle River: Pearson Education; 2013. [Google Scholar]
- 13.Arnett JJ. Clark University poll of emerging adults: Working, education, and identity. Clark University. 2015 www.clarku.edu/clark-poll-emerging-adults/pdfs/2015-clark-poll-report.pdf. Accessed 15 May 2017.
- 14.Geographical Mobility Database. United States Census Bureau. 2016 www.census.gov/data/tables/2016/demo/geographic-mobility/cps-2016.html. Accessed 15 May 2017.
- 15.Arnett JJ, Schwab J. The Clark University poll of emerging adults: Thriving, struggling, & hopeful. Clark University; 2012. http://www2.clarku.edu/clark-poll-emerging-adults/pdfs/clark-university-poll-emerging-adults-findings.pdf. Accessed 15 May 2017. [Google Scholar]
- 16.Scott ME, Schelar E, Manlove J, Cui C. Young adult attitudes about relationships and marriage: Times may have changed but expectations remain high. Child Trends. 2009 https://www.childtrends.org/wp-content/uploads/2009/07/Child_Trends-2009_07_08_RB_YoungAdultAttitudes.pdf. Accessed 15 May 2017.
- 17.Rauer AJ, Pettit GS, Lansford JE, Bates JE, Dodge KA. Romantic relationship patterns in young adulthood and their developmental antecedents. Dev Psychol. 2013;49:2159–2171. doi: 10.1037/a0031845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA. 2004;101:8174–8179. doi: 10.1073/pnas.0402680101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Sowell ER, Thompson PM, Toga AW. Mapping changes in the human cortex throughout the span of life. Neuroscientist. 2004;10:372–392. doi: 10.1177/1073858404263960. [DOI] [PubMed] [Google Scholar]
- 20.Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci. 1999;2:859–861. doi: 10.1038/13154. [DOI] [PubMed] [Google Scholar]
- 21.Vukman KB. Developmental Differences in Metacognition and their Connections with Cognitive Development in Adulthood. J Adult Dev. 2005;12:211–221. doi: 10.1007/s10804-005-7089-6. [DOI] [Google Scholar]
- 22.Johnson SB, Blum RW, Giedd JN. Adolescent maturity and the brain: the promise and pitfalls of neuroscience research in adolescent health policy. J Adolesc Health. 2009;45:216–221. doi: 10.1016/j.jadohealth.2009.05.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Targeted approaches to weight control for young adults. National Institutes of Health; 2008. https://grants.nih.gov/grants/guide/rfa-files/RFA-HL-08-007.html. Accessed 15 May 2017. [Google Scholar]
- 24.Loria CM, Signore C, Arteaga SS. The need for targeted weight-control approaches in young women and men. Am J Prev Med. 2010;38:233–235. doi: 10.1016/j.amepre.2009.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.National college health assessment II: Reference group executive summary. American College Health Association. 2011 http://www.acha-ncha.org/docs/ACHA-NCHA-II_ReferenceGroup_ExecutiveSummary_Spring2011.pdf. Accessed 15 May 2017.
- 26.Duffey KJ, Gordon-Larsen P, Jacobs DR, Williams OD, Popkin BM. Differential associations of fast food and restaurant food consumption with 3-y change in body mass index: The Coronary Artery Risk Development in Young Adults Study. Am J Clin Nutr. 2007;85:201–208. doi: 10.1093/ajcn/85.1.201. [DOI] [PubMed] [Google Scholar]
- 27.Niemeier HM, Raynor HA, Lloyd-Richardson EE, Rogers ML, Wing RR. Fast food consumption and breakfast skipping: Predictors of weight gain from adolescence to adulthood in a nationally representative sample. J Adolesc Health. 2006;39:842–849. doi: 10.1016/j.jadohealth.2006.07.001. [DOI] [PubMed] [Google Scholar]
- 28.Huffman L, West DS. Readiness to change sugar sweetened beverage intake among college students. Eat Behav. 2007:10–14. doi: 10.1016/j.eatbeh.2006.04.005. [DOI] [PubMed] [Google Scholar]
- 29.Results from the 2015 national survey on drug use and health. Substance Abuse and Mental Health Services Administration; 2016. https://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.pdf. Accessed 15 May 2017. [PubMed] [Google Scholar]
- 30.Nelson MC, Story M, Larson NI, Neumark-Sztainer D, Lytle LA. Emerging adulthood and college-aged youth: An overlooked age for weight-related behavior change. Obesity (Silver Spring) 2008;16:2205–2211. doi: 10.1038/oby.2008.365. [DOI] [PubMed] [Google Scholar]
- 31.Physical activity. Healthy People 2020. www.healthypeople.gov/2020/topics-objectives/topic/physical-activity. Accessed 15 May 2017.
- 32.Caspersen CJ, Pereira MA, Curran KM. Changes in physical activity patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc. 2000;32:1601–1609. doi: 10.1097/00005768-200009000-00013. [DOI] [PubMed] [Google Scholar]
- 33•.LaRose JG, Guthrie KM, Lanoye A, Tate DF, Robichaud E, Caccavale LJ, et al. A mixed methods approach to improving recruitment and engagement of emerging adults in behavioural weight loss programs. Obes Sci Pract. 2016;2:341–354. doi: 10.1002/osp4.71. This paper synthesizes results across 2 studies—one focus group study and one quantitative survey—and draws conclusions regarding effective methods for recruiting and engaging emerging adults age 18-25 in weight loss programs. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Nelson MC, Kocos R, Lytle LA, Perry CL. Understanding the perceived determinants of weight-related behaviors in late adolescence: A qualitative analysis among college youth. J Nutr Educ Behav. 2009;41:287–292. doi: 10.1016/j.jneb.2008.05.005. [DOI] [PubMed] [Google Scholar]
- 35.Greaney ML, Less FD, White AA, Dayton SF, Riebe D, Blissmer B, et al. College students’ barriers and enablers for healthful weight management: A qualitative study. J Nutr Educ Behav. 2009;41:281–286. doi: 10.1016/j.jneb.2008.04.354. [DOI] [PubMed] [Google Scholar]
- 36.Maslowsky J, Ozer EJ. Developmental trends in sleep duration in adolescence and young adulthood: Evidence from a national United States sample. J Adolesc Health. 2014;54:691–7. doi: 10.1016/j.jadohealth.2013.10.201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Multiple jobholding over the past two decades. Bureau of Labor Statistics; 2015. https://www.bls.gov/opub/mlr/2015/article/multiple-jobholding-over-the-past-two-decades-2.htm. Accessed 15 May 2017. [Google Scholar]
- 38.Vargas PA. The link between inadequate sleep and obesity in young adults. Curr Obes Rep. 2016;5:38–50. doi: 10.1007/s13679-016-0186-y. [DOI] [PubMed] [Google Scholar]
- 39.Marucci-Wellman HR, Lombardi DA, Willetts JL. Working multiple jobs over a day or a week: Short-term effects on sleep duration. Chronobiol Int. 2016;33:630–49. doi: 10.3109/07420528.2016.1167717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Steptoe A, Peacey V, Wardle J. Sleep duration and health in young adults. Arch Intern Med. 2006;166:1689–92. doi: 10.1001/archinte.166.16.1689. [DOI] [PubMed] [Google Scholar]
- 41.Hart CN, LaRose JG, Fava J, James B, Wing RR. The association between time in bed and obesity risk in young adults. Behav Sleep Med. 2013;11:321–327. doi: 10.1080/15402002.2012.700289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Jackson CL, Redline S, Emmons KM. Sleep as a potential fundamental contributor to disparities in cardiovascular health. Annu Rev Public Health. 2015;18:417–440. doi: 10.1146/annurev-publhealth-031914-122838. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med. 2004;141:846–850. doi: 10.7326/0003-4819-141-11-200412070-00008. [DOI] [PubMed] [Google Scholar]
- 44.Markwald RR, Melanson EL, Smith MR, Higgins J, Perreault L, Eckel RH, et al. Impact of insufficient sleep on total daily energy expenditure, food intake, and weight gain. Proc Natl Acad Sci USA. 2013;110:5695–5700. doi: 10.1073/pnas.1216951110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ford ES, Li C, Wheaton AG, Chapman DP, Perry GS, Croft JB. Sleep duration and body mass index and waist circumference among U.S. adults. Obesity (Silver Spring) 2014;22:598–607. doi: 10.1002/oby.20558. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Stress by generation. American Psychological Association; 2012. http://www.apa.org/news/press/releases/stress/2012/generations.aspx. Accessed 15 May 2017. [Google Scholar]
- 47.Kessler RC, Walters EE. Epidemiology of DSM-III-R major depression and minor depression among adolescents and young adults in the National Comorbidity Survey. Depress Anxiety. 1998;7:3–14. doi: 10.1002/(sici)1520-6394(1998)7:1<3::aid-da2>3.0.co;2-f. [DOI] [PubMed] [Google Scholar]
- 48.Major depression among adults. National Institute of Mental Health; 2015. www.nimh.nih.gov/health/statistics/prevalence/major-depression-among-adults.shtml. Accessed 15 May 2017. [Google Scholar]
- 49.Druss BG, Hoff RA, Rosenheck RA. Underuse of antidepressants in major depression: Prevalence and correlates in a national sample of young adults. J Clin Psychiatry. 2000;61:234–7. [PubMed] [Google Scholar]
- 50.Charmandari E, Tsigos C, Chrousos G. Endocrinology of the stress response. Annu Rev Physiol. 2005;67:259–284. doi: 10.1146/annurev.physiol.67.040403.120816. [DOI] [PubMed] [Google Scholar]
- 51.Torres SJ, Nowson CA. Relationship between stress, eating behavior, and obesity. Nutrition. 2007;23:887–894. doi: 10.1016/j.nut.2007.08.008. [DOI] [PubMed] [Google Scholar]
- 52.Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: The development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med. 1999;29:563–570. doi: 10.1006/pmed.1999.0585. [DOI] [PubMed] [Google Scholar]
- 53.Forman E, Butryn M, Manasse S, Crosby R, Goldstein S, Wyckoff E, et al. Acceptance-based versus standard behavioral treatment for obesity: Results from the mind your health randomized controlled trial. Obesity (Silver Spring) 2016;24:2050–2056. doi: 10.1002/oby.21601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Corsica JA, Hood MM. Eating disorders in an obesogenic environment. J Am Diet Assoc. 2011;111:996–1000. doi: 10.1016/j.jada.2011.04.011. [DOI] [PubMed] [Google Scholar]
- 55.King BM. The modern obesity epidemic, ancestral hunter-gatherers, and the sensory/reward control of food intake. Amer Psychol. 2013;68:88–96. doi: 10.1037/a0030684. [DOI] [PubMed] [Google Scholar]
- 56.Anderson DA, Shapiro JR, Lundgren JD. The freshman year of college as a critical period for weight gain: An initial evaluation. Eat Behav. 2003;4:363–367. doi: 10.1016/S1471-0153(03)00030-8. [DOI] [PubMed] [Google Scholar]
- 57.Larson NI, Perry CL, Story M, Neumark-Sztainer D. Food preparation by young adults is associated with better diet quality. J Am Diet Assoc. 2006;106(12):2001–2007. doi: 10.1016/j.jada.2006.09.008. [DOI] [PubMed] [Google Scholar]
- 58.Back to school statistics. National Center for Education Statistics; https://nces.ed.gov/fastfacts/display.asp?id=372. Accessed 15 May 2017. [Google Scholar]
- 59.Leahey TM, Gokee LaRose J, Fava JL, Wing RR. Social influences are associated with BMI and weight loss intentions in young adults. Obesity (Silver Spring) 2011;1:1157–1162. doi: 10.1038/oby.2010.301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Leahey TM, Doyle CY, Xu X, Bihuniak J, Wing RR. Social networks and social norms are associated with obesity treatment outcomes. Obesity (Silver Spring) 2015;23:1550–1554. doi: 10.1002/oby.21074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Fitzpatrick S, Gilbert S, Serpell L. Systematic review: Are overweight and obese individuals impaired on behavioural tasks of executive functioning? Neuropsychol Rev. 2013;23:138–156. doi: 10.1007/s11065-013-9224-7. [DOI] [PubMed] [Google Scholar]
- 62.Limbers CA, Young D. Executive functions and consumption of fruits/vegetables and high saturated fat foods in young adults. J Health Psychol. 2015;20:602–611. doi: 10.1177/1359105315573470. [DOI] [PubMed] [Google Scholar]
- 63.Hofmann W, Schmeichel BJ, Baddeley AD. Executive functions and self-regulation. Trends Cogn Sci. 2012;16:174–180. doi: 10.1016/j.tics.2012.01.006. [DOI] [PubMed] [Google Scholar]
- 64.Wing RR, Tate DF, Gorin AA, Raynor HA, Fava JL. A self-regulation program for maintenance of weight loss. N Engl J Med. 2006;355:1563–1571. doi: 10.1056/NEJMoa061883. [DOI] [PubMed] [Google Scholar]
- 65.Gokee-LaRose J, Gorin AA, Wing RR. Behavioral self-regulation for weight loss in young adults: a randomized controlled trial. Int JBehav Nutr Phys Act. 2009;6 doi: 10.1186/1479-5868-6-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Lau JS, Adams SH, Boscardin WJ, Irwin CE., Jr Young adults’ health care utilization and expenditures prior to the Affordable Care Act. J Adolesc Health. 2014;54:663–671. doi: 10.1016/j.jadohealth.2014.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Park MJ, Paul Mulye T, Adams SH, Brindis CD, Irwin CE., Jr The health status of young adults in the United States. J Adolesc Health. 2006;39:305–317. doi: 10.1016/j.jadohealth.2006.04.017. [DOI] [PubMed] [Google Scholar]
- 68.Fortuna RJ, Robbins BW, Halterman JS. Ambulatory care among young adults in the United States. Ann Intern Med. 2009;151:379–385. doi: 10.7326/0003-4819-151-6-200909150-00002. [DOI] [PubMed] [Google Scholar]
- 69•.Committee on Improving the Health, Safety, and Well-Being of Young Adults; Board on Children, Youth, and Families; Institute of Medicine; National Research Council. The health care system. In: Bonnie RJ, Stroud C, Breiner H, editors. Investing in the health and well-being of young adults. Washington (DC): National Academies Press; p. 2015. This resource highlights health-related challenges unique to emerging adulthood and provides recommendations for intervention development and policy decisions. [PubMed] [Google Scholar]
- 70.Ozer EM, Urquhart JT, Brindis CD, Park MJ, Irwin CE., Jr Young adult preventive health care guidelines: There but can’t be found. Arch Pediatr Adolesc Med. 2012;166:240–247. doi: 10.1001/archpediatrics.2011.794. [DOI] [PubMed] [Google Scholar]
- 71.Antognoli EL, Smith KJ, Mason MJ, Milliner BR, Davis EM, Harris-Haywood S, et al. Direct observation of weight counselling in primary care: Alignment with clinical guidelines. Clin Obes. 2014;4:69–76. doi: 10.1111/cob.12050. [DOI] [PubMed] [Google Scholar]
- 72.Cole AM, Keppel GA, Andrilla HA, Cox CM, Baldwin LM, et al. Primary care patients’ willingness to participate in comprehensive weight loss programs. From the WWAMI region practice and research network. J Am Board Fam Med. 2016;29:572–580. doi: 10.3122/jabfm.2016.05.160039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Altieri MS, Tuppo C, Telem DA, Herlihy D, Cottell K, Pryor AD. Predictors of a successful medical weight loss program. Surg Obes Relat Dis. 2015;11:431–435. doi: 10.1016/j.soard.2014.09.019. [DOI] [PubMed] [Google Scholar]
- 74.Yoong SL, Carey M, Sanson-Fisher R, Grady A. A systematic review of behavioural weight-loss interventions involving primary-care physicians in overweight and obese primary-care patients (1999–2011) Public Health Nutr. 2013;16:2083–2099. doi: 10.1017/S1368980012004375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Booth HP, Prevost TA, Wright AJ, Gulliford MC. Effectiveness of behavioural weight loss interventions delivered in a primary care setting: A systematic review and meta-analysis. Fam Pract. 2014;31:643–653. doi: 10.1093/fampra/cmu064. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Martin CK, Talamini L, Johnson A, Hymel AM, Khavjou O. Weight loss and retention in a commercial weight-loss program and the effect of corporate partnership. Int J Obes (Lond) 2010;34:742–50. doi: 10.1038/ijo.2009.276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Gudzune KA, Doshi RS, Mehta AK, Chaudhry ZW, Jacobs DK, Vakil RM, et al. Efficacy of commercial weight-loss programs: an updated systematic review. Ann Intern Med. 2015;162:501–512. doi: 10.7326/M14-2238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Hemmingsson E, Johansson K, Eriksson J, Sundström J, Neovius M, Marcus C. Weight loss and dropout during a commercial weight-loss program including a very-low-calorie diet, a low-calorie diet, or restricted normal food: Observational cohort study. Am J Clin Nutr. 2012;96:953–961. doi: 10.3945/ajcn.112.038265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.LaRose JG, Leahey TM, Hill JO, Wing RR. Differences in motivations and weight loss behaviors in young adults and older adults in the National Weight Control Registry. Obesity (Silver Spring) 2013;21:449–453. doi: 10.1002/oby.20053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Gokee-LaRose J, Gorin AA, Raynor HA, Laska MN, Jeffery RW, Levy RL, et al. Are standard behavioral weight loss programs effective for young adults? Int J Obes (Lond) 2009;33:1374–1380. doi: 10.1038/ijo.2009.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Coday M, Richey P, Thomas F, Tran QT, Terrell SB, Tylavsky F, et al. The recruitment experience of a randomized clinical trial to aid young adult smokers to stop smoking without weight gain with interactive technology. Contemp Clin Trials Commun. 2016;15:61–68. doi: 10.1016/j.conctc.2015.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Jakicic JM, King WC, Marcus MD, Davis KK, Helsel D, Rickman AD, et al. Short-term weight loss with diet and physical activity in young adults: The IDEA study. Obesity (Silver Spring) 2015;23:2385–2397. doi: 10.1002/oby.21241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Fernandez ID, Groth SW, Reschke JE, Graham ML, Strawderman M, Olson CM. eMoms: Electronically-mediated weight interventions for pregnant and postpartum women. Study design and baseline characteristics. Contemp Clin Trials. 2015;43:63–74. doi: 10.1016/j.cct.2015.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Corsino L, Lin PH, Batch BC, Intille S, Grambow SC, Bosworth HB, et al. Recruiting young adults into a weight loss trial: report of protocol development and recruitment results. Contemp Clin Trials. 2013;35:1–7. doi: 10.1016/j.cct.2013.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Tate DF, LaRose JG, Griffin LP, Erickson KE, Robichaud EF, Perdue L, et al. Recruitment of young adults into a randomized controlled trial of weight gain prevention: Message development, methods, and cost. Trials. 2014;15 doi: 10.1186/1745-6215-15-326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Patrick K, Marshall SJ, Davila EP, Kolodziejczyk JK, Fowler JH, Calfas KJ, et al. Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART) Contemp Clin Trials. 2014;37:10–18. doi: 10.1016/j.cct.2013.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Moe SG, Lytle LA, Nanney MS, Linde JA, Laska MN. Recruiting and retaining young adults in a weight gain prevention trial: Lessons learned from the CHOICES study. Clin Trials. 2016;13:205–213. doi: 10.1177/1740774515605084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Poobalan AS, Aucott LS, Precious E, Crombie IK, Smith WC. Weight loss interventions in young people (18 to 25 year olds): A systematic review. Obes Rev. 2010;11:580–592. doi: 10.1111/j.1467-789X.2009.00673.x. [DOI] [PubMed] [Google Scholar]
- 89.LaRose JG, Tate DF, Lanoye A, Fava JL, Jelalian E, Blumenthal M, et al. Adapting evidence-based behavioral weight loss programs for emerging adults: A pilot randomized controlled trial. J Health Psychol. 2017 doi: 10.1177/1359105316688951. epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Linde JA, Sevcik SM, Petrich CA, Gardner JK, Laska MN, Lozano P, et al. Translating a health behavior change intervention for delivery to 2-year college students: The importance of formative research. Transl Behav Med. 2014;4:160–169. doi: 10.1007/s13142-013-0243-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Gupta A, Calfas KJ, Marshall SJ, Robinson TN, Rock CL, Huang JS, et al. Clinical trial management of participant recruitment, enrollment, engagement, and retention in the SMART study using a Marketing and Information Technology (MARKIT) model. Contemp Clin Trials. 2015;42:185–195. doi: 10.1016/j.cct.2015.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.LaRose JG, Gorin AA, Bean MK, Lanoye A, Fava JL, Robinson EM, et al. Using motivational interviewing to enhance engagement in a weight loss program targeting emerging adults: Findings from a randomized controlled pilot trial. Poster presentation at The Obesity Society Annual Meeting; 2016; New Orleans, LA. [Google Scholar]
- 93.Merchant G, Weibel N, Pina L, Griswold WG, Fowler JH, Ayala GX, et al. Face-to-face and online networks: College students’ experiences in a weight loss trial. J Health Commun. 2017;22:75–83. doi: 10.1080/10810730.2016.1250847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94•.Godino JG, Merchant G, Norman GJ, Donohue MC, Marshall SJ, Fowler JH, et al. Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): A 2 year, parallel-group, randomised, controlled trial. Lancet Diabetes Endocrinol. 2016;4:747–755. doi: 10.1016/S2213-8587(16)30105-X. Project SMART is one of seven EARLY trials targeting weight control in young adults age 18–35. This paper presents main outcomes from their randomized controlled trial testing technology-based delivery of a weight loss intervention among college students. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Gokee LaRose J, Leahey TM, Weinberg BM, Kumar R, Wing RR. Young adults’ performance in a low-intensity weight loss campaign. Obesity (Silver Spring) 2012;20:2314–2316. doi: 10.1038/oby.2012.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96•.Svetkey LP, Batch BC, Lin PH, Intille SS, Corsino L, Tyson CC, et al. Cell phone intervention for you (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology. Obesity (Silver Spring) 2015;23:2133–2141. doi: 10.1002/oby.21226. CITY is another EARLY trial testing a technology-based delivery method for weight loss in young adults. This paper presents findings from their randomized controlled trial comparing an entirely technology-mediated intervention to one delivered in-person and supplemented with technology (i.e., smartphone self-monitoring) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97•.Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D, et al. Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA. 2016;316:1161–1171. doi: 10.1001/jama.2016.12858. IDEA is also one of the EARLY trials targeting weight loss in young adults. Investigators developed an intervention delivered first in-person, then via intervention website, text messages, and brief phone calls. Randomly assigned participants were given a wearable physical activity tracker. This paper presents main outcome findings 2 years post-baseline. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Lytle LA, Moe SG, Nanney MS, Laska MN, Linde JA. Designing a weight gain prevention trial for young adults: The CHOICES study. Am J Health Educ. 2014;45:67–75. doi: 10.1080/19325037.2013.875962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99•.Lytle LA, Laska MN, Linde JA, Moe SG, Nanney MS, Hannan PJ, et al. Weight-gain reduction among 2-year college students: The CHOICES RCT. Am J Prev Med. 2017;52:183–191. doi: 10.1016/j.amepre.2016.10.012. The CHOICES trial, also part of the EARLY Consortium, targeted 2-year college students for weight gain prevention via a one-credit college course (offered in-person, online, or in combination) followed by a social networking and support website. This paper presents the main outcomes from the CHOICES trial. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Laska MN, Sevcik SM, Moe SG, Petrich CA, Nanney MS, Linde JA, et al. A 2-year young adult obesity prevention trial in the US: Process evaluation results. Health Promot Int. 2016;31:793–800. doi: 10.1093/heapro/dav066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Wing RR, Tate D, Espeland M, Gorin A, LaRose JG, Robichaud EF, et al. Weight gain prevention in young adults: Design of the study of novel approaches to weight gain prevention (SNAP) randomized controlled trial. BMC Public Health. 2013;13:300. doi: 10.1186/1471-2458-13-300. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102•.Wing RR, Tate DF, Espeland MA, Lewis CE, LaRose JG, Gorin AA, et al. Innovative self-regulation strategies to reduce weight gain in young adults: The Study of Novel Approaches to Weight Gain Prevention (SNAP) randomized clinical trial. JAMA Intern Med. 2016;176:755–762. doi: 10.1001/jamainternmed.2016.1236. The SNAP Trial was also included in the EARLY Trials Consortium. Investigators developed two interventions to minimize weight gain in young adulthood: their large-changes approach aimed to induce weight loss to buffer against future weight gain, while their small-changes approach focused on small, daily changes to promote energy imbalance. This paper presents outcomes of both interventions compared to control over an average of 3 years of follow up. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Leahey TM, LaRose JG, Lanoye A, Fava JF, Wing RR. Secondary data analysis from a randomized trial examining the effects of small financial incentives on intrinsic and extrinsic motivation for weight loss. Health Psychol Behav Med. doi: 10.1080/21642850.2016.1276460. Forthcoming. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.LaRose JG, Wing RR. Lifestyle approaches to obesity treatment. In: Rios MS, Ordovas LM, Gutierrez Fuentes JA, editors. Obesity. Barcelona, Spain: Elsevier; 2011. pp. 311–22. [Google Scholar]
- 105.Wing RR, Tate D, LaRose JG, Gorin AA, Erickson K, Robichaud EF, et al. Frequent self-weighing as part of a constellation of healthy weight control practices in young adults. Obesity (Silver Spring) 2015;23:943–949. doi: 10.1002/oby.21064. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.LaRose JG, Lanoye A, Tate DF, Wing RR. Frequency of self-weighing and weight loss outcomes within a brief lifestyle intervention targeting emerging adults. Obes Sci Pract. 2016;2:88–92. doi: 10.1002/osp4.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Napolitano MA, Hayes S, Bennett GG, Ives AK, Foster GD. Using Facebook and text messaging to deliver a weight loss program to college students. Obesity (Silver Spring) 2013;21:25–31. doi: 10.1002/oby.20232. [DOI] [PubMed] [Google Scholar]
- 108.Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307:491–497. doi: 10.1001/jama.2012.39. [DOI] [PubMed] [Google Scholar]