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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. For 
ȠǦȅșǓ�ΛǩȠǦ� ǹΛ�^�^�ΛǦȅ�ǾƺΡ� ǹƺǉǷ�Йǿƺǿǉǩƺǹ� ƺǿǏ�ǉȅǾ-
munity resources, research should examine the ben-
ǓЙȠ� ȅǟ� ȖǓǏȣǉǩǿǠ� ǈƺȖȖǩǓȖș� Ƞȅ� șȣǉǦ� ȖǓșȅȣȖǉǓș� Κǩƺ� ǹȅǉƺǹ�
programs or providing continuous, free, or low-cost 
access to WLM treatment programs. Overall, if indi-
viduals are provided with personalized WLM support 
aligned with their levels of SES and EF, they may de-
velop the necessary skills to succeed in lifetime WLM. 

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