49 Graduate Student Journal of Psychology 2022 Vol. 19 Copyright 2022 by the Department of Counseling and Clinical Psychology Teachers College, Columbia University Executive Function in Weight Loss Maintenance: The Moderating Role of Socioeconomic Status Few individuals with overweight/obesity maintain weight loss. Executive function (EF) and socioeconomic status (SES) contribute to weight loss maintenance (WLM). This study examined whether the relationship between EF ƺǿǏ�v@E�ǏǩАǓȖș�ƺljȖȅșș�^�^ঀ�'ȅȖȠΡেǟȅȣȖ�ȒƺȖȠǩljǩȒƺǿȠș�LjǓȠΛǓǓǿࢳࢴ�েࢹࢸ�ΡǓƺȖș�ȅǟ�ƺǠǓ�ΛǓȖǓ�ƺșșǓșșǓǏ�઄ࢲ�েΡǓƺȖ�ȒȅșȠে�LjǓǦƺΚ- ioral obesity intervention. Those who achieved >5% weight loss during the program were recruited for the present study. Participants (N = 44) previously lost >5% of initial body weight. Hierarchical regressions tested the mod- erating role of SES in the relationship between performance-based EF [Iowa Gambling Task (IGT)] or self-report EF [Behavior Rating Inventory of Executive Function (BRIEF-A)] and %WLM. The relationship between perfor- mance-based EF and %WLM varied across SES (p < .05). For those with high SES, a 1-point T-score increase on 2(e�ljȅȖȖǓșȒȅǿǏǓǏ�ΛǩȠǦࢵ�ঀࢶઔ�ǠȖǓƺȠǓȖ�ઔv@E�শݾ�઀�ঀࢳࢶॹ�Ȓ�઀�ঀࢴࢱষঀ�Fȅ�ƺșșȅljǩƺȠǩȅǿ�Λƺș�ȅLjșǓȖΚǓǏ�ǟȅȖ�ȠǦȅșǓ�ΛǩȠǦ�ǹȅΛ�^�^� শݾ�઀�েঀࢳࢲॹ�Ȓ�઀�ঀࢵࢶষঀ�'ȅȖ�ȠǦȅșǓ�ΛǩȠǦ�ǹȅΛ�^�^ॹ�ǠȖǓƺȠǓȖ��'�ǾƺΡ�ǿȅȠ�LjǓǿǓЙȠ�v@Eঀ�'ȅȖ�ȠǦȅșǓ�ΛǩȠǦ�ǦǩǠǦ�^�^ॹ�ǠȖǓƺȠǓȖ��'� ǾƺΡ�LjǓǿǓЙȠ�v@Eঀ�XǓȖșȅǿƺǹǩΦǓǏ�v@E�ǩǿȠǓȖΚǓǿȠǩȅǿș�ƺljljȅȣǿȠǩǿǠ�ǟȅȖ�ǹǓΚǓǹș�ȅǟ�^�^�ƺǿǏ��'�ǾƺΡ�LjǓșȠ�ǟƺljǩǹǩȠƺȠǓ�v@Eঀ� Keywords: executive function, weight loss maintenance, socioeconomic status, obesity Approximately one-third of U.S. adults have obesity, with prevalence estimates increasing each year (Lundeen et al., 2018). Behavioral treatment for obesity is the gold-standard approach (Butryn et al., 2011). However, only half of the individuals achieve ljǹǩǿǩljƺǹǹΡ� șǩǠǿǩЙljƺǿȠ� ΛǓǩǠǦȠ� ǹȅșș� শǩঀǓঀॹ� �ઔষࢶં ȠǦȖȅȣǠǦ� these interventions (Ball & Crawford, 2002; Chris- tian et al., 2010; Kraschnewski et al., 2010; Montesi et al., 2016). Further, only 20% of individuals maintain ljǹǩǿǩljƺǹǹΡ� șǩǠǿǩЙljƺǿȠ� ΛǓǩǠǦȠ� ǹȅșș� �েΡǓƺȖࢲં ȒȅșȠেȠȖǓƺȠ- ment (Wing & Phelan, 2005), highlighting the signif- icant challenge of weight loss maintenance (WLM). A multitude of factors contribute to WLM, many of which relate to patients’ socioeconomic and demographic characteristics (Fitzgibbon et al., 2012; Goode et al., 2017). One review demonstrat- ed that occupation, education, and income all pre- dicted weight change over time, with more socio- economically disadvantaged participants having a greater risk of weight gain (Ball & Crawford, 2005). Because each of these constructs was related to poor- er weight maintenance, it may be advantageous to utilize a measure of SES that captures the broader construct of SES related to weight maintenance (Ball ૭��ȖƺΛǟȅȖǏॹࢶࢱࢱࢳ�ষঀ�eǦǓșǓ�ЙǿǏǩǿǠș�ȒȅǩǿȠ�ȠȅΛƺȖǏ�ȠǦǓ� ǩǾȒȅȖȠƺǿljǓ� ȅǟ� ǩǏǓǿȠǩǟΡǩǿǠ� ȠǦǓ� ljȅǾLjǩǿǓǏ� ǩǿМȣǓǿljǓ� of several measures of SES to understand the holistic ǩǿМȣǓǿljǓ� ȅǟ� ƺ� ǏǩșƺǏΚƺǿȠƺǠǓǏ� LjƺljǷǠȖȅȣǿǏ� ȅǿ�v@Eঀ Additionally, several psychological variables, in- cluding executive function (EF), have been implicat- ed in weight regain. EF refers to neuropsychological processing that controls and coordinates behaviors and cognitive abilities (Diamond, 2013). This typ- ically includes skills pertaining to organization and regulation such as problem solving, decision making, reasoning, attention, planning, and time manage- ǾǓǿȠঀ��ǓЙljǩȠș� ǩǿ� ǩǾȒȣǹșǓ� ljȅǿȠȖȅǹ� শ(ǩǓǹ� ǓȠ� ƺǹঀॹ� �ষࢸࢲࢱࢳ and related EF constructs have been repeatedly asso- ljǩƺȠǓǏ� ΛǩȠǦ� ȖǓǏȣljǓǏ� ȅLjǓșǩȠΡ� ȠȖǓƺȠǾǓǿȠ� ǓГljƺljΡ� ƺǿǏ� greater weight regain (Montesi et al., 2016; Wing & Phelan, 2005; Elfhag & Rössner, 2005; Varkevisser et al., 2019). Further, constructs consistently related to executive dysfunction, such as binge eating (Bog- giano et al., 2014; Striegel-Moore et al., 1998), eating in the absence of hunger, and emotional eating, have been associated with a greater weight regain (Giel et al., 2017; Elfhag & Rössner, 2005). Together, these studies suggest that EF plays a critical role in WLM. � ^�^�ƺǿǏ��'�ǾƺΡ�ǩǿȠǓȖƺljȠ�Ƞȅ�ƺАǓljȠ�ǦǓƺǹȠǦ�ȅȣȠljȅǾǓș� as well. For example, in an intervention that trained EF skills, SES moderated improvement in EF skills, such that those from low SES families experienced greater improvement than those from high SES families, em- phasizing the importance of including SES as a mod Kathryn P. King,1,2 Casie H. Morgan,1 Gareth R. Dutton,3 Sylvie Mrug,1 Alena C. Borgatti,1,3 and Marissa A. Gowey, 2 1 Department of Psychology, University of Alabama at Birmingham 2 Department of Pediatrics, University of Alabama at Birmingham 3Department of Medicine, University of Alabama at Birmingham 50 erator, rather than simply a covariate when examining ȠǦǓ�ǓАǓljȠș�ȅǟ��'�ȅǿ�ȠȖǓƺȠǾǓǿȠ�শ^ljǦȣLjǓȖȠॹࢷࢲࢱࢳ�ষঀ��Κǩ- dence from qualitative research supports this notion as well. One study exploring factors associated with di- etary behavior indicated that low and mid-SES wom- Ǔǿ�ǓǾȒǦƺșǩΦǓǏ�ȠǦǓ�ǓАǓljȠ�ȅǟ�ǓǾȒǹȅΡǾǓǿȠেȖǓǹƺȠǓǏ�ȠǩǾǓ� constraints on food preparation more than high-SES women (Inglis et al., 2005). Similarly, low-SES women, but not mid or high-SES women, named the cost of healthy food most frequently among food purchasing considerations (Inglis et al., 2005). These emphases re- МǓljȠ�ƺ�ǦǩǠǦ�ǏǓǾƺǿǏ�ǟȅȖ�ȖǓșȅȣȖljǓ�ǾƺǿƺǠǓǾǓǿȠ�Κǩƺ�ȅȖǠƺ- nization and planning when preparing and purchasing foods (Inglis et al., 2005). Indeed, healthy food prepa- ration can require a great deal of time and EF. Those with greater SES resources may be able to compensate for EF constraints by utilizing higher cost strategies (e.g., eating healthier quickly prepared foods due to lack of cost barrier and endorsing more opportunities to cook from home; Inglis et al., 2005) to accomplish EF-demanding health behaviors. Thus, these individ- uals may not experience the same degree of negative ǓАǓljȠș� ȅǟ��'�ȅǿ� ȠǦǓǩȖ�v@Eঀ��ȅǿΚǓȖșǓǹΡॹ� ȠǦȅșǓ�ΛǩȠǦ� low SES may not be able to employ more costly coping strategies (Inglis et al., 2005) and subsequently experi- ǓǿljǓ�ǠȖǓƺȠǓȖ�ǿǓǠƺȠǩΚǓ�ǓАǓljȠș�ȅǟ��'�ǏǩГljȣǹȠǩǓș�ȅǿ�v@Eঀ Although initial evidence suggests that SES may ǩǿȠǓȖƺljȠ� ΛǩȠǦ� �'� Ƞȅ� ǩǿМȣǓǿljǓ� ǦǓƺǹȠǦ� LjǓǦƺΚǩȅȖ� ȅȖ� WLM, the literature has yet to examine this moder- ƺȠǩȅǿ� ǓАǓljȠঀ� �ΚƺǹȣƺȠǩǿǠ� ȠǦǓ� ǩǿȠǓȖƺljȠǩȅǿ� LjǓȠΛǓǓǿ� ^�^� and EF on WLM would elucidate risk and resilience factors in WLM and has the potential to inform precision medicine approaches to WLM (e.g. iden- ȠǩǟΡǩǿǠ� ΛǦȅ� ǾƺΡ� LjǓǿǓЙȠ� ǟȖȅǾ� ǩǿȠǓȖΚǓǿȠǩȅǿș� ȠƺȖǠǓȠ- ing resources and/or EF skills). As such, the present paper aims to examine whether SES moderates the relationship between EF and WLM in a racially-di- verse group of individuals who lost a clinically sig- ǿǩЙljƺǿȠ� ƺǾȅȣǿȠ� ȅǟ�ΛǓǩǠǦȠ� Κǩƺ� ǹǩǟǓșȠΡǹǓ�ǾȅǏǩЙljƺȠǩȅǿঀ� We hypothesized that higher EF will be associated with greater WLM among those with low SES, but be unrelated to WLM among those with high SES. Method Participants Forty-four participants between 32-78 years of age (M = 57.43 years, SD = 11.71) were recruited from previous participants of a behavioral obesity interven- tion. The original intervention, Improving Weight Loss Maintenance Through Alternative Schedules of Treatment (ImWeL, NCT02487121), consisted of weekly sessions involving evidence-based dietary ǾȅǏǩЙljƺȠǩȅǿșॹ� ǩǿljȖǓƺșǓǏ�ȒǦΡșǩljƺǹ� ƺljȠǩΚǩȠΡॹ� ƺǿǏ�LjǓǦƺΚ- ioral strategies designed to promote adherence to these lifestyle changes, delivered by trained interventionists (for more information, see Gowey et al., 2021). For the original intervention, participants were recruited ȠǦȖȅȣǠǦ� ȠǦǓ� ǹȅljƺǹ� ǿǓΛșȒƺȒǓȖॹ� ȠǓǹǓΚǩșǩȅǿॹ� МΡǓȖșॹ� ƺǿǏ� ȠǦǓ� ȣǿǩΚǓȖșǩȠΡেƺГǹǩƺȠǓǏ� ΛǓLjșǩȠǓ� ƺǿǏ� ǓেǿǓΛșǹǓȠȠǓȖ� ƺǏ- vertisements. For the current study, participants were contacted 2-4 years post-intervention on a rolling basis for six months. Individuals were eligible for recruit- ǾǓǿȠ�ǩǟ�઄ࢶ�ઔ�ΛǓǩǠǦȠ�ǹȅșș�Λƺș�ƺljǦǩǓΚǓǏ�ǏȣȖǩǿǠ�2ǾvǓ@ঀ� �ǹǩǠǩLjǩǹǩȠΡ� Λƺș� ljȅǿЙȖǾǓǏ� LjƺșǓǏ� ȅǿ� șȠȣǏΡ� ȖǓljȅȖǏș� ȅǟ� weight loss history. Participants were excluded if they had (a) a history of bariatric surgery, (b) unintentional weight loss since participating in the previous weight ǹȅșș�ȠȖǩƺǹॹ�ȅȖ�শljষ�ƺ�ǾǓǏǩljƺǹ�ljȅǿǏǩȠǩȅǿ�ǩǿМȣǓǿljǩǿǠ�LjȅǏΡ� weight. The current sample was predominantly female and racially diverse (93% female, 55% African Ameri- can/other, 45% White, see Table 1). The study was ap- proved by the university’s Institutional Review Board. Procedure Individuals were recruited via mailed letters and telephone calls to assess eligibility. All 44 participants contacted for this study were interested and eligible to enroll in the study. They were scheduled for a two-hour study visit where informed consent procedures were conducted, after which anthropometry measurements were taken, surveys were completed, and EF testing was conducted by a trained graduate student under the supervision of a PhD-level clinical psychologist. Measures Demographic information Participants self-reported their age, educational attainment, medical history, race, ethnicity, marital status, and household income. Socioeconomic Status SES was measured by averaging standardized in- come and education variables (e.g., Pu & Rodriguez, 2021; Rodriguez et al., 2021; Gardner et al., 2017). Education was reported on a 5-point scale, rang- ing from (1) Less than a high school diploma to (5) Graduate school. Annual total gross family income KING ET AL. 51 EF AND SES IN WEIGHT LOSS MAINTENANCE was reported on an 11-point scale, with the follow- ing values: 0) Under $10,000, 1) $10-20,000, 2) $20- 30,000, 3) $30-40,000, 4) $40-50,000, 5) $50-60,000, 6) $60-70,000, 7) $70-80,000, 8) $80-90,000, 9) $90- 100,000, 10) Over $100,000. For interaction analyses, simple slopes were calculated at 1 standard deviation above and below the mean according to best practices for moderation analyses when there are no meaning- ful cut points available (Memon et al., 2019). Thus, “High SES” refers to an SES level one standard devi- ation above the mean, or the 84th percentile. “Low SES” refers to an SES level at one standard deviation below the mean, or the 16th percentile. For reference, an income one SD above the mean would be an in- come between $80-90,000 and an income one SD be- low the mean would be an income of about $30,000. For education, one SD above the mean represents a doctoral or professional degree, while one SD be- low the mean represents some college, but no degree. Anthropometric measurements � eȖƺǩǿǓǏ� șȠƺА� ǾǓƺșȣȖǓǏ� ȒƺȖȠǩljǩȒƺǿȠșঢ়� ǦǓǩǠǦȠ� ƺǿǏ� weight with shoes removed using a wall-mounted sta- diometer and digital scale. Percent weight loss maintenance (%WLM) To determine %WLM, the following data were self-reported by participants: the most weight they lost in their lifetime (initial weight loss; Krueger & Reit- her, 2015; Santos et al., 2017) how much they weighed prior to losing that weight (start weight), how much they weighed after losing that weight (post weight), and their current weight which was measured objec- tively (see anthropometric measurements section). The following formula is based on prior literature (Ryder et al., 2005) and was used to calculate %WLM: initial weight loss – (current weight – post weight) initial weight loss Performance-based EF The Iowa Gambling Task (IGT; Bechara, 2007) was utilized to measure performance-based EF. The IGT measures decision-making using four virtual decks of cards. The participant is instructed to win as much money as possible and that cards will re- ward or penalize them. Participants are scored based on their use of good decks, which provide smaller rewards more often and have better net outcomes, versus bad decks, which provide larger rewards less often and have poorer net outcomes. A norm-refer- enced T-score (age-, gender-, race-, ethnicity-matched) is generated based on the total net score, with lower scores indicating more impaired decision making. Mixed results have been noted when comparing IGT performance to performance on other executive functions, decision making, and memory tasks, with impairments in cognitive skills more associated with “cold” decision making a likely cause for the incon- sistencies (Buelow & Suhr, 2009). However, there is evidence to demonstrate that IGT shows good con- struct validity with some measures of executive func- tion and decision-making, like the Wisconsin Sorting Card task (Brand et al., 2007, Buelow & Suhr, 2009) Self-reported EF The Behavior Rating Inventory of Executive Functioning (BRIEF-A) is a standardized self-report scale of EF that is well-validated and has demonstrat- ed good internal consistency in adults with obesity (Roth et al., 2005; Rouel et al., 2016). There were ǾȅǏǓȖƺȠǓ�Ƞȅ�ǦǩǠǦ�ljȅǓГljǩǓǿȠ�ƺǹȒǦƺș�ǟȅȖ�ȠǦǓ�ǿǩǿǓ�ljǹǩǿ- ǩljƺǹ� șljƺǹǓș� শݽ� ઀� �ষॹࢳࢺঀࢱ৅ࢶࢷঀࢱ ƺǿǏ� ǦǩǠǦ� ƺǹȒǦƺș� ǟȅȖ� ȠǦǓ� ȠǦȖǓǓ� ljȅǾȒȅșǩȠǓ� șljƺǹǓș� শݽ� ઀� �ॹࢴࢺঀࢱ �ॹࢶࢺঀࢱ ƺǿǏ� �ॹࢸࢺঀࢱ ȖǓ- spectively). Three subscales showed internal consis- ȠǓǿljΡ�LjǓǹȅΛ�ȠǦǓ�ǓΠȒǓljȠǓǏ�ΚƺǹȣǓ�ȅǟࢱ�ঀࢱࢹ�শݽ�઀ࢱ�ঀࢶࢷॹݽ��઀� �ॹࢹࢸঀࢱ ��઀ݽ �ষঀࢺࢸঀࢱ XƺȖȠǩljǩȒƺǿȠș� ȖƺȠǓ� ȠǦǓ� ǟȖǓȕȣǓǿljΡ�ΛǩȠǦ� which certain behaviors have been a problem in the past month. Scoring of the 75-item questionnaire generates T-scores for the Global Executive Compos- ite (GEC). Higher scores indicate more impaired EF. Data Analyses Descriptive statistics characterized key variables. Two candidate covariates (BMI, duration of WLM) were examined via correlations. Potential covariates ȠǦƺȠ� șǩǠǿǩЙljƺǿȠǹΡ� ljȅȖȖǓǹƺȠǓǏ� ΛǩȠǦ� ઔv@E� ΛǓȖǓ� ȖǓ- tained in the model. Moderation was tested in a hi- erarchical linear regression model. Step one included BMI as a covariate, step two added mean-centered SES and EF, and step three added the interaction be- tween SES and EF. The hierarchical model was run separately for performance-based and self-reported �'ঀ� ^ǩǠǿǩЙljƺǿȠ� ǩǿȠǓȖƺljȠǩȅǿș� ΛǓȖǓ� ǟȅǹǹȅΛǓǏ� ȣȒ� ΛǩȠǦ�� simple slope testing at low and high SES (one standard deviation below and above the mean). All assump- tions and analyses were tested via SPSS version 25. 52 Results Preliminary Analyses Descriptive statistics for key variables are report- ǓǏ� ǩǿ� eƺLjǹǓ� �ঀࢲ XƺȖȠǩljǩȒƺǿȠș� ǷǓȒȠ� ȅА� ƺȒȒȖȅΠǩǾƺȠǓǹΡ� 13% of the total weight they lost in their lifetime on average. Spearman’s correlations between potential covariates (BMI and duration of WLM) and pri- mary variables of interest only revealed a negative correlation between BMI and %WLM (r = -.41, p < .01) (See Table 2). As expected, SES and Education were moderately associated (r = .36, p < .05) Thus, BMI was included as a covariate in the main analy- ses. All relevant assumptions for moderation using hierarchical multiple regression were tested and met. Moderation Analyses Performance-based EF (IGT) The hierarchical regression model testing SES as a moderator of the relationship between IGT and ઔv@E�Λƺș�șǩǠǿǩЙljƺǿȠॹ�[ࢳ�઀ࢱ�ঀࢵࢳॹ�'শࢵॹࢺࢴ�ষ�઀ࢴ�ঀࢲࢲॹ�Ȓ� ઃ�ঀࢶࢱআ�șǓǓ�eƺLjǹǓࢴ�ঀ�2ǿ�ȠǦǓ�ЙȖșȠ�șȠǓȒॹ�ǦǩǠǦǓȖ��E2�ȒȖǓǏǩljȠ- ǓǏ�ǹȅΛǓȖ�ઔv@Eॹݾ��઀�੽ࢱঀࢸࢴॹ�Ȓ�ઃ�ঀࢶࢱআ�[ࢳ�઀ࢱ�ঀࢵࢲॹ�'শࢲॹ� 42) = 6.72, p < .05. In the second step, IGT and SES ǏǩǏ�ǿȅȠ�ȣǿǩȕȣǓǹΡ�ȒȖǓǏǩljȠ�ઔv@Eॹ�۹[ࢳ�઀ࢱ�ঀࢳࢱॹ�۹'শࢳॹ� ��ঀ�2ǿ�șȠǓȒ�ȠǦȖǓǓॹ�ǦȅΛǓΚǓȖॹࢲࢲॹ�Ȓ�઀�ঀࢱࢵঀࢱ�ষ�઀ࢱࢵ'�șǩǠǿǩЙ- ljƺǿȠǹΡ�ǩǿȠǓȖƺljȠǓǏ�ΛǩȠǦ�^�^�ǩǿ�ȒȖǓǏǩljȠǩǿǠ�ઔv@Eॹݾ��઀� �ॹ�Ȓ�ઃࢲࢴঀࢱ ঀࢶࢱআ�۹[ࢳ�઀ࢱ�ঀࢺࢱॹ�۹'শࢲॹ� �ॹ�Ȓ�઀�ઃࢸࢵঀࢵ�ষ�઀ࢺࢴ .05, b = 0.31, p < .05. Simple slope analyses showed ƺ� ȒȅșǩȠǩΚǓ� ǓАǓljȠ� ȅǟ� �'� ȅǿ�v@E� ƺȠ� ǦǩǠǦǓȖ� ǹǓΚǓǹș� ȅǟ� SES, a one-point t-score increase in IGT correspond- ǓǏ�ΛǩȠǦ�ƺࢵ�ঀࢶઔ�ǩǿljȖǓƺșǓ�ǩǿ�ઔv@E�শݾ�઀ࢱ�ঀࢳࢶॹ�Ȓ�ઃ�ঀࢶࢱষॹ� while at lower levels of SES, there was no relation- șǦǩȒ�LjǓȠΛǓǓǿ� 2(e�ƺǿǏ�ઔv@E�শݾ�઀� েࢱঀࢳࢲॹ�Ȓ�઀� ঀࢵࢶষআ� șǓǓ�'ǩǠȣȖǓ� �ঀ���ȒȅșȠেǦȅlj�ȒȅΛǓȖࢲ ƺǿƺǹΡșǩș� ǟȅȖ� ȠǦǓ�Йǿƺǹ� ǾȅǏǓǹ�ǏǓǾȅǿșȠȖƺȠǓǏ�ȠǦƺȠ�ΛǩȠǦ�۹[ࢳ�઀ࢱ�ঀࢺࢱॹ�ǟࢳ�઀ࢱ�ঀࢱࢲॹ� F�઀� �ॹࢵࢵ �ݽ ઀� �ॹࢶࢱঀࢱ ƺǿǏ� ǟȅȣȖ� ȒȖǓǏǩljȠȅȖșॹ� ȠǦǓ� ƺljǦǩǓΚǓǏ� ȒȅΛǓȖ� Ƞȅ� ǏǓȠǓljȠ� ȠǦǓ� ǾȅǏǓȖƺȠǩȅǿ� ǓАǓljȠ� Λƺș� ঀࢷࢷঀࢱ Self-reported EF (BRIEF) The hierarchical regression model testing SES as a moderator of the relationship between the BRIEF ƺǿǏ�ઔv@E�Λƺș�ǿȅȠ�șǩǠǿǩЙljƺǿȠॹ�[ࢳ�઀ࢱ�ঀࢶࢲॹ�'শࢵॹࢺࢴ�ষ� ઀� �ॹࢷࢷঀࢲ Ȓ�઀� ঀࢹࢲআ� șǓǓ�eƺLjǹǓ� �ঀࢴ 2ǿ� ȠǦǓ�ЙȖșȠ� șȠǓȒॹ� ǦǩǠǦǓȖ� �E2�ȒȖǓǏǩljȠǓǏ�ǹȅΛǓȖ�ઔv@Eॹݾ��઀�েࢱঀࢹࢴॹ�Ȓ�ઃ�ঀࢶࢱআ�[ࢳ� = 0.14, F(1, 42) = 6.72, p < .05. In the second step, ȠǦǓ� �[2�'� ƺǿǏ� ^�^� ǏǩǏ� ǿȅȠ� șǩǠǿǩЙljƺǿȠǹΡ� ȒȖǓǏǩljȠ� ઔv@Eॹ�۹[ࢳ�઀ࢱ�ঀࢲࢱॹ�۹'শࢳॹࢱࢵ�ষ�઀ࢱ�ঀࢷࢲॹ�Ȓ�઀� ঀࢶࢹঀ� � 2ǿ� șȠǓȒ� ȠǦȖǓǓॹ� ȠǦǓ� �[2�'� ǏǩǏ� ǿȅȠ� șǩǠǿǩЙljƺǿȠǹΡ� ǩǿȠǓȖ- ƺljȠ� ΛǩȠǦ� ^�^� Ƞȅ� ȒȖǓǏǩljȠ� ઔv@Eॹ� �ݾ ઀� ঀࢴࢱॹ� Ȓ� ઀� ঀࢷࢹআ� �ॹࢲॹ�۹'শࢲࢱࢱঀࢱ��઀ࢳ]۹ �ষ�઀ࢺࢴ ঀࢴࢱॹ� Ȓ�઀� ঀࢷࢹॹ� Lj�઀� ঀࢷࢳࢱॹ� Ȓ� ઀� ঀࢷࢹঀ��� ȒȅșȠেǦȅlj� ȒȅΛǓȖ� ƺǿƺǹΡșǩș� ǟȅȖ� ȠǦǓ� Йǿƺǹ�ǾȅǏ- Ǔǹ�ǏǓǾȅǿșȠȖƺȠǓǏ� ȠǦƺȠ�ΛǩȠǦ�۹[ࢳ�઀ࢱ�ঀࢲࢱࢱॹ� ǟࢳ�઀ࢱ�ঀࢲࢱࢱॹ� F�઀� �ॹࢵࢵ �ݽ ઀� �ॹࢶࢱঀࢱ ƺǿǏ� ǟȅȣȖ� ȒȖǓǏǩljȠȅȖșॹ� ȠǦǓ� ƺljǦǩǓΚǓǏ� ȒȅΛǓȖ� Ƞȅ� ǏǓȠǓljȠ� ȠǦǓ� ǾȅǏǓȖƺȠǩȅǿ� ǓАǓljȠ� Λƺș� ঀࢹࢱঀࢱ Discussion The goal of the present study was to examine the degree to which SES moderates the relationship be- tween EF and WLM to address gaps in the WLM lit- erature that may inform precision medicine approach- es. Given recent studies demonstrating relationships between SES, EF, and weight loss outcomes, we ex- ƺǾǩǿǓǏ�ΛǦǓȠǦǓȖ�ǩǿǏǩΚǩǏȣƺǹș�ǟȖȅǾ�ǏǩАǓȖǓǿȠ�^�^�LjƺljǷ- grounds showed unique relationships between EF and %WLM. EF was measured via a performance-based test and self-reports, as these methods provide unique information about EF and do not correlate highly with each other (Garcia et al., 2013; Toplak et al., 2013). As ǓΠȒǓljȠǓǏॹ�ЙǿǏǩǿǠș� ǩǿǏǩljƺȠǓǏ� ȠǦƺȠ� ȠǦǓ� ȖǓǹƺȠǩȅǿșǦǩȒ�LjǓ- tween performance-based EF and %WLM was depen- dent on SES; contrary to our expectation, however, ȠǦȅșǓ�ΛǩȠǦ�ǦǩǠǦ�^�^� ǓΠȒǓȖǩǓǿljǓǏ� ƺ� ǠȖǓƺȠǓȖ�LjǓǿǓЙȠ�ȅǟ� performance-based EF on %WLM than individuals with low SES. Regarding self-reported EF, our hy- pothesis was not supported, as SES and self-reported �'�ǏǩǏ�ǿȅȠ� ǩǿȠǓȖƺljȠ� Ƞȅ�ƺАǓljȠ�ƺǿ� ǩǿǏǩΚǩǏȣƺǹঢ়ș�ઔv@Eঀ� Access to high-cost coping strategies in high-SES individuals may best explain the unique relation be- tween EF and SES in high-SES individuals. It is like- ly that for high SES individuals, having access to an abundance of weight management resources (e.g., grocery stores, gym memberships/classes, meal prepa- ration services, smartphone applications, and gadgets, ǓȠljঀষ�ǾƺΡ�LjǓ�ǾȅȖǓ�ǓГljǩǓǿȠǹΡ�ƺljljǓșșǓǏ�ƺǿǏ�ȣȠǩǹǩΦǓǏ�ǟȅȖ� an individual with stronger EF skills. For example, in- dividuals with higher SES may be more likely to own a wearable device to monitor activity, and those with stronger EF skills may be more likely to utilize the track- ing features (e.g., weight, food, and exercise tracking) on the device or its associated phone app. Alternative- ǹΡॹ��'�ǏǓЙljǩȠș�ljȅȣǹǏ�ƺǹșȅ�LjǓ�ȣǿǩȕȣǓǹΡ�ǦǩǿǏǓȖǩǿǠ�ȠǦȅșǓ� with high SES, perhaps due to increased access to un- healthy foods and mismanagement of extra resources. In contrast, lower SES individuals often lack ba- sic access to these same resources (Ailshire & House, �ষআࢲࢲࢱࢳ ȠǦȣșॹ� �'�ǾƺΡ�ǾƺǿǩǟǓșȠ� ǏǩАǓȖǓǿȠǹΡ� ǩǿ� ǓƺljǦ� ȅǟ� these scenarios. For higher SES individuals with abun- KING ET AL. 53 dant opportunities, there is a need to organize options, utilize self-control with grocery shopping, and manage ǾǓǾLjǓȖșǦǩȒș�ǓГljǩǓǿȠǹΡ�ƺǿǏ�ƺljljȣȖƺȠǓǹΡঀ� � ��ǹȠǓȖǿƺȠǩΚǓ- ly, for lower SES individuals maintaining weight loss, there are fewer resources through which to apply EF skills of coordinating, organizing, and managing, so EF abilities may have a more limited “range” of impact. In fact, for low SES individuals, the weight-loss interven- tion program itself may be the primary resource acces- sible to this group for healthy eating and activity. Once the program ends, these individuals may not have the community structures (e.g., gyms, healthy food mar- ǷǓȠșॹ� ǓȠljঀষ� ǩǿ� ȒǹƺljǓ� Ƞȅ� șȣȒȒȅȖȠ� ȒȖǓΚǩȅȣș� ǓАȅȖȠșঀ� eǦǩș� interpretation is supported by the recent emphasis on the relationship between social determinants of health and adverse health outcomes (Medvedyuk et al., 2018). The use of a performance-based EF task is a no- ȠƺLjǹǓ�șȠȖǓǿǠȠǦ�ȅǟ�ȠǦǓ�șȠȣǏΡ�ǏǓșǩǠǿঀ�2(e�ǩș�șȒǓljǩЙljƺǹǹΡ� ǏǓșǩǠǿǓǏ� Ƞȅ�ǏǓȠǓljȠ� ǏǓljǩșǩȅǿেǾƺǷǩǿǠ�ǏǓЙljǩȠș� ƺǿǏ�ǏȅǓș� șȅ� ǩǿ� ȠǦǓ� ljȅǿȠǓΠȠ� ȅǟ� Йǿƺǿljǩƺǹ� Ǡƺǩǿș� ƺǿǏ� ǹȅșșǓș� শ�Ǔ- chara, 2007). One interpretation of these outcomes could imply unique interactions between SES and a ЙǿƺǿljǩƺǹǹΡেȅȖǩǓǿȠǓǏ��'েǏǓȒǓǿǏǓǿȠ�ȠƺșǷঀ��ǹȠǦȅȣǠǦ�ȠǦǓ� correlation between IGT and SES was weak and non- șǩǠǿǩЙljƺǿȠ� শșǓǓ� eƺLjǹǓ� �ষॹࢳ ȠǦǓȖǓ� ƺȖǓ� Йǿƺǿljǩƺǹ� ȒƺȠȠǓȖǿș� ƺljȖȅșș� ǏǩАǓȖǓǿȠ� ^�^� ǠȖȅȣȒș� ȠǦƺȠ� ƺȖǓ�ΛȅȖȠǦΡ� Ƞȅ� ǿȅȠǓঀ� For example, individuals with low SES experience ǟȖǓȕȣǓǿȠ� Йǿƺǿljǩƺǹ� ȣǿljǓȖȠƺǩǿȠǩǓș� ΛǦǩljǦ� ȅǟȠǓǿ� ȒȖǓș- ent as stressors and constraints, rather than solvable complications (Chen & Miller, 2013). In the context of weight management, which can be characterized as a stressor due to the extensive behavior change, resource allotment, and commitment required to maintain success, if low-SES families are attempt- ǩǿǠ� Ƞȅ� LjƺǹƺǿljǓ�ΛǓǩǠǦȠেȖǓǹƺȠǓǏ� șȠȖǓșșȅȖș�ΛǩȠǦ� Йǿƺǿljǩƺǹ� stressors, a “spiral of resource loss” (Hobfoll, 2001) can occur (e.g., a parent misses work to take care of a șǩljǷ�ljǦǩǹǏॹ�ǹȅșǓș�ƺ�ǴȅLjॹ�ljƺǿঢ়Ƞ�ƺАȅȖǏ�ǠΡǾ�ǾǓǾLjǓȖșǦǩȒষঀ� eǦǩș�Йǿƺǿljǩƺǹ�ȣǿljǓȖȠƺǩǿȠΡ�ǾƺΡ�ǹǓƺǏ�Ƞȅ�LjȣΡǩǿǠ�ljǦǓƺȒ- er, unhealthy foods or lower quantities of healthy foods. Thus, real-world decisions about money, food ljǦȅǩljǓșॹ�ƺǿǏ�ǦǓƺǹȠǦΡ�ƺljljǓșș�Ƞȅ�ǟȅȅǏ�ljȅȣǹǏ�LjǓ�ǩǿМȣǓǿlj- ing behavior during this performance-based measure ƺǿǏ�ǩǿМȣǓǿljǩǿǠ�ǹǩǟǓșȠΡǹǓ�ljǦȅǩljǓș�ǩǿ�ȠǦǓ�ȖǓƺǹেΛȅȖǹǏ�șǓȠ- ȠǩǿǠॹ� ƺǾȒǹǩǟΡǩǿǠ� ȠǦǓ� șǩǠǿǩЙljƺǿȠ� ǏǩАǓȖǓǿljǓ� ǟȅȖ� ǦǩǠǦǓȖ� SES individuals compared to lower SES individuals. Self-reported EF was measured using the BRIEF-A questionnaire and is considered more of a global com- ȒȅșǩȠǓ�ȅǟ�ǏǩАǓȖǓǿȠ�LjǓǦƺΚǩȅȖș�ȒǓȖȠƺǩǿǩǿǠ�Ƞȅ��'�ƺLjǩǹǩȠǩǓșঀ� Subjective rating scales tend to have more ecological validity than performance-based testing but can be șȠǩМǓǏ�ǩǟ�șȅǾǓȅǿǓ�Ǧƺș�șǓΚǓȖǓ�ǓǿȅȣǠǦ�ǩǾȒƺǩȖǾǓǿȠș�ȠǦƺȠ� ȠǦǓΡ� ƺȖǓ� ǿȅȠ� ƺΛƺȖǓ� ȅǟ� ȠǦǓǩȖ� ǏǓЙljǩȠș� ȅȖ� ȅǟ� ȠǦǓ� ǩǾȒƺljȠ� ȠǦǓșǓ�ǏǓЙljǩȠș�ǦƺΚǓ�ȅǿ�ǓΚǓȖΡǏƺΡ�LjǓǦƺΚǩȅȖ�শ�ƺȖǷǹǓΡॹࢳࢲࢱࢳ�আ� Chan, 2008). A self-rating scale that requires insight into one’s own cognitive abilities may be inherently ǏǩГljȣǹȠ� ǟȅȖ� șȅǾǓȅǿǓ�ΛǩȠǦ� ǩǾȒƺǩȖǾǓǿȠ� ǩǿ� șǓǹǟেƺΛƺȖǓ- ness as compared to performance-based testing which is rated by a trained observer (Buchanan, 2016), which could explain some of the discrepancies between the performance-based and self-reported EF results. Limitations This study has some limitations that should be mentioned. One of the most important limitations is the sample size, which reduced statistical power and did not now allow more complex modeling tech- niques, such as additional predictors or covariates. The ȒȅșȠ�Ǧȅlj�ƺǿƺǹΡșǓș�ȖǓΚǓƺǹǓǏ�ǹȅΛ�ȒȅΛǓȖ�Ƞȅ�ǏǓȠǓljȠ�ǓАǓljȠșॹ� supporting the notion that a larger sample size may improve power and allow for more complex modeling. Given that this study enrolled only those who lost a ljǹǩǿǩljƺǹǹΡ�șǩǠǿǩЙljƺǿȠ�ƺǾȅȣǿȠ�ȅǟ�ΛǓǩǠǦȠॹ�ǟȣȠȣȖǓ�șȠȣǏǩǓș� ȣșǩǿǠ� șǩǾǩǹƺȖ� ǏǓșǩǠǿș� ǾƺΡ� LjǓǿǓЙȠ� ǟȖȅǾ� ȅΚǓȖেȖǓljȖȣǩȠ- ment during a weight loss intervention to allow for a larger recruitment pool of those who lose a clinically șǩǠǿǩЙljƺǿȠ�ƺǾȅȣǿȠ�ȅǟ�ΛǓǩǠǦȠঀ��ǹȠǓȖǿƺȠǩΚǓǹΡॹ�ǟȣȠȣȖǓ�ȖǓ- search could consider more large-scale designs, such as that of the National Weight Control Registry (Hill et al., 2005). However, with this approach, measurements would need to be adapted for remote data collection, which would introduce another limitation in exchange for an increased sample size. A second limitation was the composition of the sample. The majority of the șƺǾȒǹǓ�Λƺș�ǟǓǾƺǹǓॹ�ǹǩǾǩȠǩǿǠ�ȠǦǓ�ǠǓǿǓȖƺǹǩΦƺLjǩǹǩȠΡ�ȅǟ�ЙǿǏ- ings to weight loss experiences for males. Despite these limitations, the current study represents an important step toward prioritizing SES and EF in weight manage- ment interventions and considering the impacts indi- ΚǩǏȣƺǹ� ǏǩАǓȖǓǿljǓș� ƺǿǏ� ljǦƺȖƺljȠǓȖǩșȠǩljș� ǦƺΚǓ�ȅǿ�v@Eঀ� For the present study, the best two factors to capture SES included educational history and race, however, it is understood that other variables can be included to strengthen SES as a construct. One recent study acknowledged the complexity of mea- suring and conceptualizing SES and included a sam EF AND SES IN WEIGHT LOSS MAINTENANCE 54 KING ET AL. ple of additional criteria to be considered in future research (Rodríguez-Hernández et al., 2020). Spe- ljǩЙljƺǹǹΡॹ� ȠǦǓΡ� ǦǩǠǦǹǩǠǦȠ� ȒƺȖǓǿȠƺǹ� ǓǏȣljƺȠǩȅǿॹ� ǟƺǾǩǹΡ� income, parental occupation, household resources, and neighborhood resources. Alternatively, SES can also be considered subjective, with perceived SES demonstrating its own separate impact on health outcomes (Nobles et al., 2013) compared to objec- tive components of SES. Therefore, future research should also carefully consider the conceptualization and measurement of SES when studying weight man- ƺǠǓǾǓǿȠ� ƺǿǏ� ljȅȣǹǏ� ljȅǿșǩǏǓȖ� ȠǦǓ� ǩǿМȣǓǿljǓș� ȅǟ� LjȅȠǦ� perceived SES and more objective SES factors related to actual income, occupation, and education status. � 'ȣȠȣȖǓ�șȠȣǏǩǓș�ǾƺΡ�LjǓǿǓЙȠ�ǟȖȅǾ�ǓΠȒƺǿǏǩǿǠ�ȣȒȅǿ� ȠǦǓ� ȒȖǓșǓǿȠ� ЙǿǏǩǿǠșঀ� 'ȅȖ� ǓΠƺǾȒǹǓॹ� ǓАȅȖȠș� ljȅȣǹǏ� LjǓ� ǾƺǏǓ� Ƞȅ� ȖǓljȖȣǩȠ�ǾƺǹǓș� ƺǿǏ�ǓΠƺǾǩǿǓ� șǓΠ�ǏǩАǓȖǓǿljǓș� ǩǿ� the studied relationships. Additionally, it may be ad- vantageous to recruit a mix of individuals with vary- ing degrees of success with WLM, including those experiencing weight regain. This allows for more variance in weight maintenance outcomes and allows for an improved investigation of potential barriers to v@Eঀ�KǿǓ�Йǿƺǹ�ljȅǿșǩǏǓȖƺȠǩȅǿ�ǩǿljǹȣǏǓș�ǩșȅǹƺȠǩǿǠ�ȠǦǓ� ǏǩАǓȖǓǿȠ� ljǹǩǿǩljƺǹ� ǏȅǾƺǩǿș� ljƺȒȠȣȖǓǏ� ǩǿ� ȠǦǓ� �[2�'� Ƞȅ� examine unique associations between individual EF domains, WLM, and SES. Continuation of this line of research could ultimately inform the development of precision medicine strategies that take such relation- ships into account in treatment selection and delivery. Conclusion � eǦǓ�ȒȖǓșǓǿȠ�ЙǿǏǩǿǠș� șȣǠǠǓșȠ� ȠǦƺȠ� ǟȅȖ� ȠǦȅșǓ�ΛǩȠǦ� ǦǩǠǦ� ^�^ॹ� ΛǦȅ� ƺǹȖǓƺǏΡ� ȒȅșșǓșș� Ljƺșǩlj� Йǿƺǿljǩƺǹ� ƺǿǏ� community resources, higher EF may facilitate the ƺLjǩǹǩȠΡ� Ƞȅ� ȅȖǠƺǿǩΦǓॹ� ȒȖǩȅȖǩȠǩΦǓॹ� ƺǿǏ� ǓГljǩǓǿȠǹΡ� ƺljljǓșș� available weight management tools and strategies. 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