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. Author manuscript; available in PMC: 2024 Apr 1.
Published in final edited form as: Appetite. 2023 Jan 24;183:106465. doi: 10.1016/j.appet.2023.106465

Ecological momentary assessment of changes in eating behaviors, appetite, and other aspects of eating regulation in Roux-en-Y gastric bypass and sleeve gastrectomy patients

J Graham Thomas a, Leah M Schumacher a, Sivamainthan Vithiananthan b, Daniel B Jones c, Kathryn E Smith d, Tommy Chou a, Pavlos K Papasavas e, Dale S Bond e
PMCID: PMC9975010  NIHMSID: NIHMS1869347  PMID: 36701847

Abstract

Bariatric surgery can have profound impacts on eating behaviors and experiences, yet most prior research studying these changes has relied on retrospective self-report measures with limited precision and susceptibility to bias. This study used smartphone-based ecological momentary assessment (EMA) to evaluate the trajectory of change in eating behaviors, appetite, and other aspects of eating regulation in 71 Roux-en-Y gastric bypass and sleeve gastrectomy patients assessed preoperatively and at 3, 6, and 12-months postoperative. For some outcomes, results showed a consistent and similar pattern for SG and RYGB where consumption of sweet and high-fat foods and hunger, desire to eat, ability to eat right now, and satisfaction with amount eaten all improved from pre- to 6-months post-surgery with some degree of deterioration at 12-months post-surgery. By contrast, other variables, largely related to hedonic hunger and craving and desire for specific foods, showed less consistent patterns that differed by surgery type. While the findings suggest an overall pattern of improvement in eating patterns following bariatric surgery, they also highlight how a return to preoperative habits may begin as early as 6 months after surgery. Additional research is needed to understand mechanisms that promote changes in eating behavior after surgery, and how best to intervene to preserve beneficial effects.

Keywords: Bariatric surgery, eating, appetite, behavior, ecological momentary assessment

INTRODUCTION

Bariatric surgery is a highly effective treatment for severe obesity, producing significant weight loss and improvements in numerous aspects of health (Arterburn, Telem, Kushner, & Courcoulas, 2020; Park et al., 2019). For example, sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB)—the two most frequently performed MBS procedures—produce average weight losses of 25%–31% of total body weight (Arterburn et al., 2018) and remission of diabetes in ~60% of patients at 1-year follow-up (McTigue et al., 2020). Bariatric surgery effects weight loss and health improvements via multiple mechanisms, including altered eating behaviors that drive reductions in energy intake (Miras & Le Roux, 2013; Mulla, Middelbeek, & Patti, 2018).

Bariatric surgery and related clinical care can have profound impacts on eating patterns through a variety of mechanisms. First, bariatric surgery procedures alter gastrointestinal anatomy and physiology resulting in both limitations on amount of food that can be consumed and cascading effects on multiple physiological and neurological processes implicated in regulation of eating behavior (e.g., appetite-related hormones, gut microbiota, bile acids, and neural signaling affecting hunger and satiety cues, food preferences, and food reinforcement; Al-Najim, Docherty, & le Roux, 2018; Holst et al., 2018; Mulla et al., 2018; Münzberg, Laque, Yu, Rezai-Zadeh, & Berthoud, 2015). Additionally, patients are given postoperative dietary guidelines designed to promote weight loss, ensure adequate nutrition, and avoid potential complications, which include recommendations such as eating small, balanced meals; eating and drinking slowly; and avoiding foods that are high in fat and sugar (Mechanick et al., 2020). Thus, the mechanical and physiological effects of bariatric surgery combined with clinical recommendations can impact a broad range of eating behaviors (e.g., eating frequency, meal size, types of foods eaten), as well as facets of eating regulation associated with weight and weight loss, such as dietary restraint, disinhibition (i.e., overeating in response to internal or external cues), and cravings (Al-Najim et al., 2018; Holst et al., 2018; Mulla et al., 2018; Münzberg et al., 2015). Past research demonstrates changes in multiple aspects of eating regulation and normative eating behavior (i.e., eating behaviors that all patients are expected to exhibit, to a certain extent) after bariatric surgery. For example, hunger, cravings, disinhibition, and emotional eating decrease postoperatively, whereas restraint and postprandial fullness increase (Al-Najim et al., 2018; Cena et al., 2016; Devlin et al., 2018; Morínigo et al., 2006; Pepino et al., 2014; Sarwer et al., 2008; Wong et al., 2020). Research on changes in diet quality and adherence to the postoperative diet has been relatively mixed, with some studies reporting improvements in aspects of diet quality but many revealing suboptimal adherence (e.g., continued excess consumption of fats; Sarwer et al., 2008; Zarshenas, Tapsell, Neale, Batterham, & Talbot, 2020). Other research generally shows that on average, overall maladaptive eating (e.g., binge eating, night eating, grazing) improves after surgery though may recur to a certain extent over time (Devlin et al., 2018; Williams-Kerver, Steffen, & Mitchell, 2019).

While the above findings underscore the many ways in which eating regulation and behavior may change after surgery, most studies have relied on retrospective self-report and many have assessed changes in eating regulation and behavior at a single postoperative timepoint. Retrospective measures are prone to numerous biases such as forgetting and memory heuristics (Bradburn, Rips, & Shevell, 1987; Gorin & Stone, 2001). These methodological limitations may hinder identification of important postoperative changes in eating regulation and behavior, thereby impeding understanding of how bariatric surgery affects eating regulation and behavior as well as identification of potential intervention targets to enhance its efficacy.

Additionally, while different types of procedures may exert differential effects on eating behaviors and underlying physiological processes, there has been limited comparison of changes in facets of eating regulation and eating behavior by surgery type. This is particularly important in the case of RYGB and SG given that these procedures alter the gut anatomy in distinct ways, yielding differential changes in gastrointestinal physiology that impact food and energy intake (Pucci & Batterham, 2019). For example, RYGB increases secretion of gut hormones (peptide YY 36 [PYY] and glucagon-like peptide [GLP-1]) to a greater extent than SG, whereas SG effects large reductions in ghrelin, a hormone that increases appetite and food intake (Pucci & Batterham, 2019). Thus, while RYGB and SG both alter appetite and food intake, mechanisms for these alterations may vary by surgical procedure.

To date, research has compared RYGB and SG on a variety of eating behaviors (e.g., stopping eating prior to feeling full and avoiding unhealthy snacks) and aspects of eating regulation (e.g., food preferences and cravings, dietary restraint, and disinhibition) (Guyot et al., 2022; Lewis et al., 2021; Makaronidis et al., 2022; Mou et al., 2021; Nance, Eagon, Klein, & Pepino, 2017). However, a majority of these studies have been cross-sectional, precluding evaluation of potential differential pre- to postoperative changes. Findings are mixed with two studies showing no differences in eating behaviors, emotions surrounding eating, and food liking/wanting between RYGB and SG (Guyot et al., 2022; Mou et al., 2021), and one study showing lower levels of enjoyment and cravings for high-calorie, palatable foods after RYGB versus SG (Lewis et al., 2021). Similarly, findings from studies comparing pre- to postoperative changes in key aspects of eating regulation between RYGB and SG have produced mixed findings: one study found comparable beneficial effects of RYGB and SG on several aspects of eating regulation and hedonic taste perception (Nance, Eagon, Klein, & Pepino, 2017) whereas another study found larger postoperative reductions in hedonic hunger after SG compared to RYGB (Makaronidis et al., 2022). Yet, both studies assessed changes in outcomes at a single postoperative timepoint, impeding ability to characterize and compare longitudinal postoperative changes in eating behavior and regulation after RYGB and SG.

The aforementioned gaps in the literature can be addressed, in part, via ecological momentary assessment (EMA; Shiffman, Stone, & Hufford, 2008). EMA involves repeated sampling of individuals’ behaviors and experiences in real-time as they go about their daily lives. EMA overcomes many of the limitations of retrospective self-report measures and is a valuable tool for evaluating post-surgical eating behaviors and eating regulation in naturalistic settings, particularly when employed repeatedly across time (e.g., before and at multiple post-surgical timepoints). However, to our knowledge, few studies have used EMA to evaluate and compare changes in eating behaviors after different bariatric surgery procedures (Bond et al., 2021; Ratcliff, Zeller, Inge, Hrovat, & Modi, 2014; Thomas et al., 2011; Williams-Kerver et al., 2020).

The present study used EMA to examine changes in normative eating behaviors and aspects of eating regulation among patients undergoing SG or RYGB at four time points from pre- to 12-months post-surgery. Whereas previous studies have assessed one or a few different eating behaviors and/or aspects of eating regulation after bariatric surgery, we aimed to conduct the most comprehensive evaluation of changes in normative eating behaviors and determinants of eating regulation during the initial postoperative year. Specifically, we assessed general eating behaviors (e.g., consumption of fatty meats) that patients are encouraged to avoid to prevent postoperative complications (Moizé, Pi-Sunyer, Mochari, & Vidal, 2010; Sherf Dagan et al., 2017), appetite sensations (e.g., hunger and fullness), key aspects of eating regulation (e.g., cravings, hedonic hunger or eating in the absence of hunger) shown to change postoperatively and relate to weight loss (Bond et al., 2021; Bryant, Malik, Whitford-Bartle, & Waters, 2020; Makaronidis et al., 2022; Nance, Eagon, Klein, & Pepino, 2017), as well as socio-environmental factors (e.g., location of eating and number of people present while eating) that could influence eating patterns but have been relatively neglected in prior bariatric research. Finally, we aimed to determine whether changes in the above variables differed between SG and RYGB. The study aims were exploratory in nature, and therefore no a priori hypotheses were specified.

METHODS

2.1. Design

This study involves analysis of data from a prospective cohort study that examined psychosocial and behavioral predictors of weight loss after bariatric surgery. All aspects of the protocol relevant to the current study are described below. Full details are described in Goldstein et al (2018). This study was approved by the Miriam Hospital (TMH) and Beth Israel Deaconess Medical Center (BIDMC) Institutional Review Boards.

Participants

Participants who were undergoing RYGB or SG were recruited from two university-based hospital bariatric surgery centers (TMH and BIDMC) to participate in a 12-month study. Eligibility criteria required that participants were ≥21 years of age, had severe obesity (BMI ≥ 35 kg/m2), and were undergoing RYGB or SG at either study site. Individuals were excluded if they were currently enrolled in another weight loss program or related behavioral treatment outside standard surgical care, or if they reported a condition that could interfere with adherence to measurement protocol.

2.2. Procedures

Participants were recruited from the affiliated surgery centers 3 to 8 weeks preoperatively at regularly scheduled clinic visits. They received study information from their healthcare provider and interested individuals completed a brief telephone screening to assess eligibility. Those who appeared eligible were invited to attend an in-person visit where they provided informed consent, had their height and weight measured, completed a sociodemographic questionnaire, and were trained on use of the study-related app for EMA data collection. Following the baseline visit, participants began a 10-day EMA assessment period.

Participants were provided with an Android smartphone (Samsung Galaxy S7; Samsung Electronics, South Korea) for EMA data collection that ran a customized mobile app created by PiLR Health (MEI Research, Ltd.). Participants were asked to respond to audible prompts that triggered survey questions delivered at 4 semi-random times anchored to 11:00am, 2:00pm, 5:00pm, and 8:00pm. At each prompt, participants reported on several aspects of eating regulation and behavior (described in 2.3. Measures). Participants repeated this same 10-day EMA procedure at 3-, 6-, and 12-months post-surgery. Participants received $75 in compensation for completing each assessment, plus a $.50 bonus for each completed EMA survey (approximately $25/assessment). Participants were allowed to extend the duration of the EMA period to promote data adequacy in the event that their participation was interrupted due to illness, family emergency, etc. All available data were included in analysis.

Participants at both sites (TMH and BIDMC) received standard postoperative dietary monitoring and counseling as part of their care. Dietary recommendations relevant to the current study included avoiding foods high in fat and sugar, eating balanced meals with small portions, and eating slowly (to avoid complications and promote satiety recognition; Sherf Dagan et al., 2017).

2.3. Measures

Demographic and anthropometric characteristics and surgery type.

Participants reported demographic information (e.g., age, race/ethnicity, education) by questionnaire at baseline. Height was measured by trained research staff at pre-surgery using a wall-mounted Harnpenden stadiometer and weight (to the nearest 0.1 kg) was measured using a calibrated digital scale to calculate BMI. Surgery type (RYGB vs. SG) was reported by participants and verified by clinic staff.

Eating Regulation.

Several aspects of eating regulation were assessed at each semi-random EMA survey (i.e., up to 4x/day). Disinhibition (i.e., overeating in response to internal or external cues) and dietary restraint (i.e., conscious efforts to restrict food intake to influence body weight and/or shape) were each assessed with 5 items that were adapted from the Restraint subscale of the Three Factor Eating Questionnaire (e.g., disinhibition items: “When people around me overeat, I overeat too;” restraint items: “I do not eat some foods because they make me fat;” Stunkard & Messick, 1985). Response options ranged from 1 “Never” to 5 “Always.” Responses to the 5 disinhibition items were summed to create a disinhibition score and responses to the 5 restraint items were summed to create a restraint score. Higher scores indicate greater disinhibition and restraint, respectively. Power of food (i.e., hedonic motivation to consume palatable food) was assessed with 3 items from the Power of Food Scale (e.g., “It’s very important to me that the foods I eat are as delicious as possible;” Lowe et al., 2009). Response options were anchored at 1 “Strongly Disagree” to 5 “Strongly Agree”. Responses to the three items were summed, with higher scores indicating greater hedonic influence on eating behaviors. Single items that were created de novo for this study assessed hunger (“I feel hungry”), eating in the absence of hunger (“I want to eat even though I am not hungry”), and cravings (“I have an intense desire or urge (cravings) to eat specific foods”). Each item was rated from 1 “Strongly Disagree” to 5 “Strongly Agree.”

In addition to the above items which were asked at each EMA survey, at each semi-random prompt participants were asked whether they had eaten since the last time they answered questions. If they said yes, they were also asked to respond to four items developed de novo in consultation with a registered dietician to indicate satisfaction with amount eaten (“I am satisfied with the amount of food I have eaten”), desire to eat (“I have a desire to eat”), ability to eat right now (“I think I could eat right now”), and fullness (“I am full”). Each item was rated from 1 “Strongly Disagree” to 5 “Strongly Agree.” Eating Behaviors. Several aspects of eating behavior were assessed at each EMA survey where participants reported eating since the last prompt. Participants reported whether they had planned to eat at the time of the most recent eating episode (Yes/No), the number of people present when eating, the source of the food (i.e., prepackaged, prepared at home by myself, prepared at home by someone else, purchased from a restaurant, purchased from a fast-food restaurant, purchased prepared at a grocery store, other), and eating location (i.e., home, work, school, fast-food, restaurant, other’s home, or other location). Responses to the source of food question were recoded for analyses to indicate whether or not the food was prepared at home (Yes/No) and responses to the eating location were recoded to indicate whether or not the participate ate at home (Yes/No). Participants also reported the number of servings of fatty meats (e.g., hamburger, any meat with visible fat), sweets (e.g., candy, pastries, cakes), and fried foods (including chips) eaten. All eating behavior questions were created de novo for this study.

2.4. Statistical Analysis

Data were analyzed using IBM SPSS Statistics for Windows, Version 25.0. Demographic characteristics and EMA response rates were characterized using means and standard deviations, or counts with percentages, as appropriate. General longitudinal linear mixed effect models were used to examine change in each eating variable over time (modeled as months since the baseline assessment, including a linear and quadratic effect to capture rebound in post-surgical improvements), while adjusting for covariates. The interaction between time and surgery type (RYGB vs. SG) was also evaluated to test for differential patterns of change. Individual EMA ratings were represented at level 1, which were nested within persons (level 2), to account for non-independence of observations made by each participant. Participant and time were treated as random effects and the models were implemented using an unstructured covariance matrix. Continuous outcomes were modeled using the normal distribution and an identity link function. Dichotomous outcomes were modeled using a binomial distribution and logit link function. The analytic approach allowed all participants meeting a minimum threshold of EMA adherence (≥10 observations at a given assessment period) to contribute to the analysis. This threshold was selected to ensure adequacy of the data and avoid potential bias resulting from participants completing very few surveys. All models controlled for baseline BMI and sociodemographic characteristics (age, gender, race/ethnicity, education, marital status). Critical alpha was set at 0.05 for statistical tests; a correction for multiple comparisons was not implemented due to the exploratory nature of the analysis. A 3-level modeling approach that accounted for non-independence of observations within assessment period was evaluated but not reported due to poor fit.

RESULTS

3.1. Participants

Ninety-two participants consented to participate, 77 completed the baseline assessment, and 71 provided sufficient EMA data for analysis at baseline (see additional details on survey completion below). As shown in Table 1, the average age was approximately 45 years and the average baseline BMI was approximately 45 kg/m2. Most participants identified as female (89%) and non-Hispanic (78%), approximately 42% identified as a racial identity other than White, and 38% had earned a college or graduate degree. Sociodemographic characteristics and baseline BMI did not differ among those receiving SG (n = 53) and RYGB (n = 18).

Table 1.

Sociodemographic/anthropometric characteristics for the full sample and by surgery type

Full sample (n = 71) Comparisons by Surgery Type
SG (n = 53) RYGB (n = 18) F or X2 p
Age, M (SD) 44.5 (11.2) 45.1 (10.8) 42.7 (12.3) 0.60 .44
Baseline BMI, M (SD) 45.7 (7.1) 46.0 (7.4) 45.0 (6.5) 0.27 .61
Gender, n (%) 2.89 .09
 Female 63 (88.7%) 49 (92.5%) 14 (77.8%)
 Male 8 (11.3) 4 (7.5%) 4 (22.2%)
Race, n (%) 0.02 .90
 White 41 (57.7%) 31 (58.5%) 10 (55.6%)
 African American/ Black 19 (26.8%) 16 (30.2%) 3 (16.7%)
 Native American 1 (1.4%) 1 (1.9%) 0 (0%)
 Other 13 (18.3%) 8 (15.1%) 5 (27.8%)
Ethnicity, n (%) 0.38 .54
 Non-Hispanic 55 (77.5%) 42 (79.2%) 13 (72.2%)
 Hispanic 16 (22.5%) 11 (20.8%) 5 (27.8%)
Educational attainment, n (%) 2.56 .11
 High school, GED, or less 9 (12.7%) 2 (3.8%) 7 (38.9%)
 Vocational training 3 (4.2%) 2 (3.8%) 1 (5.6%)
 beyond high school
 Some college (< 4 years) 32 (45.1%) 26 (49.1%) 6 (33.3%)
 College/university degree 15 (21.1%) 12 (22.6%) 3 (16.7%)
 Graduate or professional 12 (16.9%) 11 (20.8%) 1 (5.6%)
 education
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Note. Participants could select more than one racial identity and 1 participant did not report racial identity. Analyses assessing the associations of surgery type with racial identity and educational attainment used the following dichotomized variables: White vs. a race other than White/multiple racial identities (race) and college or less vs. obtaining a college/university or graduate degree (educational attainment). Abbreviations: BMI = body mass index, SG = sleeve gastrectomy, Roux-en-Y gastric bypass = RYGB

Seventy-one participants contributed data at baseline and 58, 50, and 44 contributed data at 3-, 6-, and 12-months postoperatively, respectively. Study completers (i.e., provided 12-month assessment data) and non-completers (i.e., did not provide 12-month assessment data) did not differ by surgery type, baseline BMI, sex, or ethnicity. However, non-completers were younger (40.2 vs. 47.1 years; p = .01) and a greater proportion identified as non-White (61.5% vs. 36.4%; p = .04). Participants completed an average of 36.2 (SD = 15.0, range 13–77) EMA ratings at baseline, 41.0 (SD = 16.5, range 10–83) at 3 months, 41.4 (SD = 14.3, range 14–97) at 6 months, and 38.7 (SD = 14.5, range 10–67) at 12-months. On average, participants reported 17.9 eating events (SD = 9.5, range 2–38) at baseline, 19.2 (SD = 9.9, range 4–43) at 3 months, 19.6 (SD = 10.1, range 4–56) at 6 months, and 18.8 (SD = 9.0, range 2–42) at 12 months.

3.2. Change in eating behaviors and aspects of eating regulation

Estimated means for measures of eating behaviors and aspects of eating regulation at each assessment period appear by surgery type in Table 2. Full model estimates are reported in Supplemental Table 1. Most variables followed a similar pattern, in which changes that might generally be considered favorable occurred from baseline to 6-months postoperative, with some degree of rebound observed at 12-months postoperative. Within the domain of eating regulation, this was true of changes in hunger (which decreased), a desire to eat (which decreased), the ability to eat right now (which decreased), and satisfaction with the amount eaten (which increased). Within the domain of eating behaviors, this was also true of consumption of fatty meats, fried foods, and sweets (which all decreased). For all of the above eating regulation and behavior variables, there was no significant difference in the pattern of change by surgery type. For other variables, participants receiving one type of surgery evidenced larger initial changes and/or more substantial rebound. Patients receiving RYGB evidenced greater change in dietary restraint (which increased), cravings (which decreased), the desire to eat in the absence of hunger (which decreased), and the number of people present when eating (which increased). In contrast, patients receiving SG evidenced greater change in disinhibition (which decreased), hedonic appetitive motivation measured via the Power of Food scale (which decreased), and fullness (which increased). The proportion of time that food was prepared at home exhibited an atypical pattern in which rates decreased for RYGB and increased for SG. The change over time was not significant for proportion of eating that was planned versus unplanned, or for the proportion of time that eating occurred at home.

Table 2.

EMA ratings of eating behaviors, attitudes, and experience among bariatric surgery patients at pre- and postoperative time points by type of surgical procedure: Roux-en-Y gastric bypass (RYGB) vs. sleeve gastrectomy (SG)

Average EMA Rating
Pre-op 3-mo. post-op 6-mo. post-op 12-mo. post-op
RYGB SG RYGB SG RYGB SG RYGB SG
Favorable Change Followed by Rebound; No Difference by Surgery Type *
 Hunger, M (SE) 2.1 (0.1) 2.2 (0.1) 1.9 (0.1) 1.9 (0.1) 1.8 (0.1) 1.7 (0.1) 2.1 (0.2) 1.9 (0.1)
 Desire to eat, M (SE) 1.8 (0.1) 1.9 (0.1) 1.7 (0.1) 1.7 (0.1) 1.6 (0.1) 1.5 (0.1) 1.8 (0.2) 1.7 (0.1)
 Could eat right now, M (SE) 2.0 (0.2) 2.1 (0.1) 1.8 (0.2) 1.7 (0.1) 1.7 (0.2) 1.6 (0.1) 1.8 (0.2) 1.8 (0.1)
 Satisfaction with amount eaten, M (SE) 4.2 (0.2) 4.0 (0.1) 4.3 (0.1) 4.3 (0.1) 4.4 (0.2) 4.4 (0.1) 4.3 (0.2) 4.3 (0.1)
 Servings of fatty meats, # (SE) 0.40 (0.1) 0.28 (0.1) 0.29 (0.1) 0.21 (0.1) 0.25 (0.1) 0.18 (0.1) 0.39 (0.1) 0.19 (0.1)
 Servings of sweets, # (SE) 0.21 (.1) 0.27 (0.1) 0.15 (0.1) 0.17 (0.1) 0.12 (0.1) 0.13 (0.1) 0.15 (0.1) 0.21 (0.1)
 Servings of fried foods, # (SE) 0.15 (0.1) 0.22 (0.1) 0.07 (0.1) 0.17 (0.1) 0.06 (0.1) 0.13 (0.1) 0.20 (0.1) 0.12 (0.1)
SG Experiences Greater Change/Rebound †
 Dietary Restraint, M (SE) 3.1 (0.1) 3.2 (0.1) 3.4 (0.1) 3.6 (0.1) 3.5 (0.2) 3.7 (0.1) 3.3 (0.3) 3.5 (0.2)
 Desire to eat in absence of hunger, M (SE) 1.8 (0.1) 1.5 (0.1) 1.6 (0.1) 1.4 (0.1) 1.5 (0.1) 1.4 (0.1) 1.6 (0.1) 1.4 (0.1)
 Cravings, M (SE) 2.1 (0.2) 1.8 (0.1) 1.8 (0.2) 1.7 (0.1) 1.7 (0.2) 1.6 (0.1) 1.7 (0.2) 1.5 (0.1)
RYGB Experiences Greater Change/Rebound ‡
 Disinhibition, M (SE) 2.3 (0.1) 2.4 (0.1) 2.1 (0.1) 2.0 (0.1) 1.9 (0.2) 1.8 (0.1) 2.1 (0.2) 2.1 (0.1)
 Power of Food, M (SE) 2.6 (0.2) 2.8 (0.1) 2.5 (0.2) 2.5 (0.1) 2.4 (0.2) 2.3 (0.1) 2.5 (0.3) 2.6 (0.2)
 Fullness, M (SE) 4.1 (0.2) 3.8 (0.1) 4.0 (0.2) 4.0 (0.1) 4.0 (0.2) 4.2 (0.1) 4.0 (0.2) 4.1 (0.1)
 Number of people present, # (SE) 1.2 (1.2) 2.9 (0.7) 4.2 (1.4) 3.6 (0.8) 5.6 (1.8) 4.1 (1.0) 3.5 (2.4) 4.7 (1.4)
RYGB Rate Decreases; SG Rate Increases §
 Food prepared at home, % 62.3 50.2 58.4 55.5 55.9 58.1 55.3 55.6
No Statistically Significant Change
 Eating was planned, % 84.1 88.5 86.6 90.7 87.4 92.4 85.0 94.6
 Ate at home, % 64.9 58.9 58.6 59.5 54.1 59.8 51.4 59.8
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Note.

*

Indicates a pattern of statistical significance in which there is favorable change through 6-months followed by rebound at 12-months, and no significance difference in trajectories between SG and RYGB.

†

Indicates a pattern of statistical significance in which SG experiences greater initial change and/or rebound.

‡

Indicates a pattern of statistical significance in which RYGB experiences greater initial change and/or rebound.

§

Indicates a pattern of statistical significance in which rates decreased for RYGB and increased for SG.

DISCUSSION

This study examined changes in normative eating behaviors and key aspects of appetite and eating regulation during the initial 12 months after bariatric surgery. In contrast with most past studies of eating behavior among patients who have undergone bariatric surgery, we used EMA to minimize retrospective recall and maximize ecological validity by capturing participants’ eating behaviors and experiences in near proximity to the time and place that they occurred. To our knowledge, this is the largest EMA study to prospectively evaluate and compare changes in eating patterns and related determinants after the two most common bariatric surgical procedures — SG and RYGB.

We found that changes in several of the eating variables exhibited a similar pattern for both SG and RYGB patients, characterized by initial improvement from pre- to 6-months post-surgery followed by some degree of deterioration between 6- and 12-months post-surgery. This pattern was evident for both consumption of foods that patients are instructed to avoid (i.e., consumption of fatty meats, fried foods, and sweets) and multiple components of appetite and eating regulation (i.e., hunger, desire to eat, ability to eat right now, and satisfaction with amount eaten).

This pattern mirrors weight loss trajectories for many bariatric surgery patients, with the largest reduction in body weight occurring during the immediate 6-months post-surgery followed by either slower weight loss, weight plateauing, or even weight regain from 6- to 12-months post-surgery (Courcoulas et al., 2013). Similarly, a recent investigation involving a subset of participants from the Longitudinal Assessment of Bariatric Surgery (LABS) study showed that mean reported total energy intake decreased by more than half from 2175 kcal/day before surgery to 985 kcal/day at 6-months post-surgery, but then increased to 1286 kcal/day between 6- and 12-months post-surgery (Raatz et al., 2020). These data, taken together with results from the current study, indicate the largest changes in overall energy intake, consumption of specific high-calorie, palatable foods, and appetite and eating regulation occur during the immediate 6 months post-surgery when weight loss is most rapid. Subsequently, attenuation of these improvements occurs between 6- and 12-months post-surgery when patients’ rate of weight loss is slowing down.

The above patterns also partially align with the “honeymoon period” described by patients as the initial 12-months after bariatric surgery when weight loss, appetite control, and limiting food consumption requires minimal effort (Bryant, Malik, Whitford-Bartle, & Waters, 2020; Jones, Cleator, & Yorke, 2016; Lynch, 2016). However, our findings suggest that the time at which the honeymoon ends and the “real work” begins likely occurs earlier for many patients, between 6- and 12-months post-surgery. Consequently, future studies should evaluate whether implementing adjunctive psychological and behavioral interventions earlier in the honeymoon period, prior to 6-months post-surgery, can help patients in better preparing for both the work that lies ahead and dispelling any notion that bariatric surgery is a panacea.

A number of eating variables did not follow the same pattern, and instead varied by surgery type. RYGB patients had greater increases in dietary restraint and larger decreases in cravings and desire to eat in the absence of hunger, compared to SG patients. By contrast, SG patients had greater increases in fullness and larger decreases in disinhibition and appetite for palatable foods compared to RYGB patients. Unlike most studies that have compared effects of RYGB and SG on eating behavior and regulation and shown no differences, our findings suggest that RYGB and SG could have differential effects on multiple aspects of appetite and eating regulation (Guyot et al., 2022; Mou et al., 2021; Nance, Eagon, Klein, & Pepino, 2017). To our knowledge, only two other studies have shown differences between RYGB and SG on similar eating variables during the initial 12 months following bariatric surgery (Lewis et al., 2021; Makaronidis et al., 2022). Similar to our findings, the first study found that participants who underwent RYGB reported less enjoyment and cravings for high-calorie, palatable foods (e.g., high-fat meats, candy, and sweet baked goods) than those who underwent SG (Lewis et al., 2021). However, unlike the current study, this previous study surveyed RYGB and SG patients at a single time point (12-months post-surgery), making it unclear whether group differences existed before surgery and if enjoyment and craving for these foods changed for each group over time. The second study found larger pre- to 6-month postoperative reductions in hedonic hunger after SG versus RYGB, opposite to what we found in the current study (Makaronidis et al., 2022). While reasons for this discrepant pattern are not exactly clear, it is noteworthy that the current study describes changes across multiple postoperative time points whereas the former study only assesses change at a single postoperative time point. It is possible that differences in eating behaviors and regulation between RYGB and SG may change over time after surgery due to diminishing effects on hormonal, metabolic and neural mechanisms as described below.

The clear differences in SG and RYGB anatomy and subsequent effects on mechanisms of eating behavior (e.g., gut-derived hormones) provide biological plausibility for our results showing differences between these surgery groups in patterns of appetite and eating regulation (Pucci & Batterham, 2019; Zakeri & Batterham, 2018). Additionally, research suggests there may be differences between RYGB and SG in neural responsivity to palatable foods, with greater changes in activation of brain reward regions to these foods after RYGB compared to SG (Baboumian et al., 2019; Faulconbridge et al., 2016). Finally, some research has also shown potential differential effects of RYGB and SG on sweet and fat taste sensitivity, which may influence hedonic evaluation of sweet and high-fat foods (Shoar, Naderan, Shoar, Modukuru, & Mahmoodzadeh, 2019); however, studies using validated sensory techniques and direct food intake measurements, rather than questionnaires, find minimal change nor differences by surgery type (Nance, Acevedo, & Pepino, 2020; Nielsen, Schmidt, le Roux, & Sjodin, 2019).

Methodological differences may also explain why we found differences between SG and RYGB in appetite and eating regulation variables where other studies did not (Guyot et al., 2022; Mou et al., 2021; Nance et al., 2017). For example, whereas previous studies were either cross-sectional or included only a single post-surgical time point (Guyot et al., 2022; Makaronidis et al., 2022; Mou et al., 2021; Nance et al., 2017), our study assessed patients before and at multiple time points across the initial 12 months following surgery, which is required to detect and characterize longitudinal patterns of change. Also, we used EMA rather than retrospective questionnaires, which enabled us to conduct repeated assessments of eating variables at and around actual eating episodes across several days at pre- and multiple post-surgical time points. These EMA capabilities and advantages may have allowed us to capture more nuanced differences in the potential impact of RYGB and SG upon factors involved in appetite and eating regulation (Engel et al., 2016; Zakeri & Batterham, 2018). Despite these methodological strengths, additional prospective studies using EMA, ideally in combination with direct measures of eating and appetite mechanisms, are needed to confirm and elucidate potential differential effects of RYGB and SG on appetite and eating regulation. Moreover, further research is needed to disentangle surgery and weight loss effects on these outcomes.

Our use of EMA also enabled collection of near real-time information about socio-environmental factors that may play a role in the eating patterns of patients who undergo bariatric surgery. Results showed that the frequency of eating at home did not change over time for both RYGB and SG. However, patients who had RYGB reported a greater increase in the number of people present during eating compared to those who had SG, suggesting greater social interaction around meals. Interestingly, frequency of home food preparation decreased for the RYGB group, but increased for the SG group. To our knowledge, these data are the first to show how different environmental factors believed to influence eating behavior change after surgery. While not an aim of the current study, future studies will evaluate the extent to which these environmental factors associate with eating behavior and influence surgical outcomes. For example, a recent study found that low degree of social eating pressure and smaller household composition associated with larger decreases in energy density of food choices during an ad libitum buffet 6 months after bariatric surgery (Nielsen et al., 2021). Such studies will aid in identifying environmental targets to support positive changes in patients’ eating behavior.

This study has several strengths. It is the largest study to date to use EMA to assess changes in patterns of normative eating behaviors and indicators of appetite and eating regulation before and during the initial 12 months after bariatric surgery. Importantly, we also compared patterns of change in these variables between patients who underwent SG and RYGB, which yielded novel findings suggestive of differential effects of these procedures on eating and appetite regulation. While the magnitude of the effects may appear small, it is important to consider that EMA ratings pertain to individual instances of behaviors and experiences, and the cumulative effect of small differences over time can be highly clinically relevant. For example, small lapses in adherence to recommended postoperative eating behaviors (e.g., avoiding fatty foods and stopping eating when full) that are repeated over time could signal gradual return to old undesirable eating habits or development of new risky eating patterns that both increase risk of weight recurrence. Moreover, by tracking with more precision the cumulative effect of small differences in daily eating patterns, EMA could help identify the level or point at which an eating pattern becomes problematic. Early detection of worsening eating patterns could prompt more timely investigation of underlying causes (e.g., lack of nutritional follow-up, recurrent depression, anatomical surgical failure) and implementation of appropriate prevention and management strategies (i.e., behavioral, dietary, pharmacological, and possibly surgical [e.g., revision]; Ansari & Elhag, 2021). Finally, the ability to detect small cumulative differences in eating behavior and regulation between RYGB and SG could be relevant for understanding variability and sustainability of metabolic and hormonal mechanisms that underlie the effects of each procedure (e.g., greater sustained increases in fullness after SG might suggest gradual downregulation of ghrelin and upregulation of PYY 36 and GLP-1; Pucci & Batterham, 2019).

Our findings should also be viewed in the context of certain limitations. While steps were taken to maximize the number of ratings obtained from each participant at each assessment point and most participants completed about 40 EMA ratings per assessment wave, there was moderate attrition over the course of the entire study, and a small number of participants reported on relatively few eating events. The latter may reflect a failure of the semi-random prompting strategy to capture some participants’ eating times and/or a failure of participants to report on their meals. This missing data may reduce the internal validity of our findings. Use of an analytic approach that incorporates all available data from all participants helps to offset this limitation. Similarly, because of intensive nature of the protocol that required completion of surveys up to 4 times per day for 10 days at 4 different assessment points, the patients who participated in this study may be more conscientious than the average patient who undergoes bariatric surgery, resulting in a selection bias. Because we compared changes in multiple eating-related outcomes between RYGB and SG without correction for multiple comparisons, findings should be treated as exploratory and for hypothesis-generating purposes. The study period was limited to the initial 12 months after surgery, precluding ability to evaluate differences in patterns of eating behavior and aspects of appetite and eating regulation during active weight loss and weight stability/weight regain. This study did not assess relations of patterns in eating and weight loss, and whether such patterns vary by surgery type. Future studies that formally test eating behaviors and aspects of eating regulation as mechanisms of post-surgical weight loss trajectory are needed.

CONCLUSION

This study is the first to use EMA to characterize and compare patterns of change in specific eating behaviors and indicators of appetite and eating regulation after SG and RYGB. For some outcomes, results showed a consistent and similar pattern for SG and RYGB where consumption of sweet and high-fat foods and hunger, desire to eat, ability to eat right now and satisfaction with amount eaten all improved from pre- to 6-months post-surgery with some degree of deterioration at 12-months post-surgery. By contrast, other variables, largely related to hedonic hunger and craving and desire for specific foods, showed less consistent patterns that differed by surgery type. Additional research is needed to understand how patterns of change in EMA-measured eating behaviors after bariatric surgery relate to biological mechanisms of eating and weight change during both active weight loss and weight loss maintenance.

Supplementary Material

1

ACKNOWLEDGEMENTS

We thank the participants for their contributions to the research.

Funding.

This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK108579; principal investigators: Drs. Dale Bond & Graham Thomas], [R01 DK113408; principal investigators: Drs. Pavlos Papasavas & Godfrey Pearlson]

Declaration of Interest:

The authors report grants from NIH/NIDDK during the conduct of the study. Dr. Thomas also reports personal fees from Lumme Health, Inc. and Medifast, Inc. The other authors have no conflicts of interest to declare.

Footnotes

ETHICS STATEMENT

This study was approved by the Institutional Review Board of The Miriam Hospital and Rhode Island Hospital (IRB Registration #s: RIH IRB 1 – 00000396, RIH IRB 2 – 00004624, TMH IRB - 00000482) with committee/project number 211215 45CFR 46.110(4)(7)(8a). Written informed consent was obtained from all participants and the study was performed in accordance with the Declaration of Helsinki.

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REFERENCES

  1. Al-Najim W, Docherty NG, & le Roux CW (2018). Food intake and eating behavior after bariatric surgery. Physiological Reviews, 98(3), 1113–1141. doi: 10.1152/physrev.00021.2017 [DOI] [PubMed] [Google Scholar]
  2. Arterburn D, Wellman R, Emiliano A, Smith SR, Odegaard AO, Murali S, … PCORnet Bariatric Study Collaborative (2018). Comparative Effectiveness and Safety of Bariatric Procedures for Weight Loss: A PCORnet Cohort Study. Ann Intern Med, 169(11), 741–750. doi: 10.7326/M17-2786 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arterburn DE, Telem DA, Kushner RF, & Courcoulas AP (2020). Benefits and risks of bariatric surgery in adults: a review. JAMA, 324(9), 879–887. doi: 10.1001/jama.2020.12567 [DOI] [PubMed] [Google Scholar]
  4. Baboumian S, Pantazatos SP, Kothari S, McGinty J, Holst J, & Geliebter A (2019). Functional Magnetic Resonance Imaging (fMRI) of Neural Responses to Visual and Auditory Food Stimuli Pre and Post Roux-en-Y Gastric Bypass (RYGB) and Sleeve Gastrectomy (SG). Neuroscience, 409, 290–298. doi: 10.1016/j.neuroscience.2019.01.061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bond DS, Thomas JG, Jones DB, Schumacher LM, Webster J, Evans EW, … Vithiananthan S (2021). Ecological momentary assessment of gastrointestinal symptoms and risky eating behaviors in Roux-en-Y gastric bypass and sleeve gastrectomy patients. Surgery for Obesity and Related Diseases, 17(3), 475–483. doi: 10.1016/j.soard.2020.11.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bradburn NM, Rips LJ, & Shevell SK (1987). Answering autobiographical questions: The impact of memory and inference on surveys. Science, 236(4798), 157–161. doi: 10.1126/science.3563494 [DOI] [PubMed] [Google Scholar]
  7. Bryant EJ, Malik MS, Whitford-Bartle T, & Waters GM (2020). The effects of bariatric surgery on psychological aspects of eating behaviour and food intake in humans. Appetite, 150, 104575. doi: 10.1016/j.appet.2019.104575 [DOI] [PubMed] [Google Scholar]
  8. Cena H, De Giuseppe R, Biino G, Persico F, Ciliberto A, Giovanelli A, & Stanford FC (2016). Evaluation of eating habits and lifestyle in patients with obesity before and after bariatric surgery: a single Italian center experience. Springerplus, 5(1), 1–9. doi: 10.1186/s40064-016-3133-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Courcoulas AP, Christian NJ, Belle SH, Berk PD, Flum DR, Garcia L, … Longitudinal Assessment of Bariatric Surgery, C. (2013). Weight change and health outcomes at 3 years after bariatric surgery among individuals with severe obesity. Jama, 310(22), 2416–2425. doi: 10.1001/jama.2013.280928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Devlin MJ, King WC, Kalarchian MA, Hinerman A, Marcus MD, Yanovski SZ, & Mitchell JE (2018). Eating pathology and associations with long-term changes in weight and quality of life in the longitudinal assessment of bariatric surgery study. International Journal of Eating Disorders, 51(12), 1322–1330. doi: 10.1002/eat.22979 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. El Ansari W, & Elhag W (2021). Weight regain and insufficient weight loss after bariatric surgery: definitions, prevalence, mechanisms, predictors, prevention and management strategies, and knowledge gaps—a scoping review. Obes Surg, 31(4), 1755–1766. doi: 10.1007/s11695-020-05160-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Engel SG, Crosby RD, Thomas G, Bond D, Lavender JM, Mason T, … Wonderlich SA (2016). Ecological Momentary Assessment in Eating Disorder and Obesity Research: a Review of the Recent Literature. Curr Psychiatry Rep, 18(4), 37. doi: 10.1007/s11920-016-0672-7 [DOI] [PubMed] [Google Scholar]
  13. Faulconbridge LF, Ruparel K, Loughead J, Allison KC, Hesson LA, Fabricatore AN, … Wadden TA (2016). Changes in neural responsivity to highly palatable foods following roux-en-Y gastric bypass, sleeve gastrectomy, or weight stability: An fMRI study. Obesity (Silver Spring), 24(5), 1054–1060. doi: 10.1002/oby.21464 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Goldstein SP, Thomas JG, Vithiananthan S, Blackburn GA, Jones DB, Webster J, … Moon J (2018). Multi-sensor ecological momentary assessment of behavioral and psychosocial predictors of weight loss following bariatric surgery: study protocol for a multicenter prospective longitudinal evaluation. BMC obesity, 5(1), 27. doi: 10.1186/s40608-018-0204-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gorin AA, & Stone AA (2001). Recall biases and cognitive errors in retrospective self-reports: A call for momentary assessments. Handbook of health psychology, 23, 405–413. [Google Scholar]
  16. Guyot E, Nazare JA, Oustric P, Robert M, Disse E, Dougkas A, & Iceta S (2022). Food Reward after Bariatric Surgery and Weight Loss Outcomes: An Exploratory Study. Nutrients, 14(3). doi: 10.3390/nu14030449 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Holst JJ, Madsbad S, Bojsen-Møller KN, Svane MS, Jørgensen NB, Dirksen C, & Martinussen C (2018). Mechanisms in bariatric surgery: gut hormones, diabetes resolution, and weight loss. Surgery for Obesity and Related Diseases, 14(5), 708–714. doi: 10.1016/j.soard.2018.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jones L, Cleator J, & Yorke J (2016). Maintaining weight loss after bariatric surgery: when the spectator role is no longer enough. Clin Obes, 6(4), 249–258. doi: 10.1111/cob.12152 [DOI] [PubMed] [Google Scholar]
  19. Lewis KH, Ji M, Bai Y, Arterburn DE, Bhakta BB, Cornejo M, … Coleman KJ (2021). Bariatric Surgical Alterations in Tolerability, Enjoyment and Cravings in the Diet (BSATED) instrument: A new scale to measure food preferences following bariatric surgery. Appetite, 162, 105151. doi: 10.1016/j.appet.2021.105151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lowe MR, Butryn ML, Didie ER, Annunziato RA, Thomas JG, Crerand CE, … Wallaert M (2009). The Power of Food Scale. A new measure of the psychological influence of the food environment. Appetite, 53(1), 114–118. doi: 10.1016/j.appet.2009.05.016 [DOI] [PubMed] [Google Scholar]
  21. Lynch A (2016). “When the honeymoon is over, the real work begins:” Gastric bypass patients’ weight loss trajectories and dietary change experiences. Soc Sci Med, 151, 241–249. doi: 10.1016/j.socscimed.2015.12.024 [DOI] [PubMed] [Google Scholar]
  22. Makaronidis JM, Pucci A, Adamo M, Jenkinson A, Elkalaawy M, & Batterham RL (2022). Impact of sleeve gastrectomy compared to Roux-en-y gastric bypass upon hedonic hunger and the relationship to post-operative weight loss. Intern Emerg Med, 17(7), 2031–2038. Doi: 10.1007/s11739-022-03063-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. McTigue KM, Wellman R, Nauman E, Anau J, Coley RY, Odor A, … PCORNet Bariatric Study Collaborative (2020). Comparing the 5-Year Diabetes Outcomes of Sleeve Gastrectomy and Gastric Bypass: The National Patient-Centered Clinical Research Network (PCORNet) Bariatric Study. JAMA Surg, 155(5), e200087. doi: 10.1001/jamasurg.2020.0087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Mechanick JI, Apovian C, Brethauer S, Garvey WT, Joffe AM, Kim J, … Seger J (2020). Clinical practice guidelines for the perioperative nutrition, metabolic, and nonsurgical support of patients undergoing bariatric procedures–2019 update: cosponsored by American Association of Clinical Endocrinologists/American College of Endocrinology, The Obesity Society, American Society for Metabolic & Bariatric Surgery, Obesity Medicine Association, and American Society of Anesthesiologists. Surgery for Obesity and Related Diseases, 16(2), 175–247. doi: 10.4158/GL-2019-0406 [DOI] [PubMed] [Google Scholar]
  25. Miras AD, & Le Roux CW (2013). Mechanisms underlying weight loss after bariatric surgery. Nature Reviews Gastroenterology & hepatology, 10(10), 575–584. doi: 10.1038/nrgastro.2013.119 [DOI] [PubMed] [Google Scholar]
  26. Moizé VL, Pi-Sunyer X, Mochari H, & Vidal J (2010). Nutritional pyramid for post-gastric bypass patients. Obes Surg, 20(8), 1133–1141. doi: 10.1007/s11695-010-0160-9 [DOI] [PubMed] [Google Scholar]
  27. Morínigo R, Moizé V, Musri M, Lacy AM, Navarro S, Marín J. L. s., … Vidal J (2006). Glucagon-like peptide-1, peptide YY, hunger, and satiety after gastric bypass surgery in morbidly obese subjects. The Journal of Clinical Endocrinology & Metabolism, 91(5), 1735–1740. doi: 10.1210/jc.2005-0904 [DOI] [PubMed] [Google Scholar]
  28. Mou D, de Vries CEE, Pater N, Poulsen L, Makarawung DJS, Wiezer MJ, … Tavakkoli A (2021). BODY-Q patient-reported outcomes measure (PROM) to assess sleeve gastrectomy vs. Roux-en-Y gastric bypass: eating behavior, eating-related distress, and eating-related symptoms. Surg Endosc, 35(8), 4609–4617. doi: 10.1007/s00464-020-07886-w [DOI] [PubMed] [Google Scholar]
  29. Mulla CM, Middelbeek RJ, & Patti ME (2018). Mechanisms of weight loss and improved metabolism following bariatric surgery. Annals of the New York Academy of Sciences, 1411(1), 53–64. doi: 10.1111/nyas.13409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Münzberg H, Laque A, Yu S, Rezai-Zadeh K, & Berthoud HR (2015). Appetite and body weight regulation after bariatric surgery. Obesity Reviews, 16, 77–90. doi: 10.1111/obr.12258 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nance K, Acevedo MB, & Pepino MY (2020). Changes in taste function and ingestive behavior following bariatric surgery. Appetite, 146, 104423. doi: 10.1016/j.appet.2019.104423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nance K, Eagon JC, Klein S, & Pepino MY (2017). Effects of Sleeve Gastrectomy vs. Roux-en-Y Gastric Bypass on Eating Behavior and Sweet Taste Perception in Subjects with Obesity. Nutrients, 10(1). doi: 10.3390/nu10010018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nielsen MS, Christensen BJ, Ritz C, Holm L, Lunn S, Taekker L, … Sjodin A (2021). Factors Associated with Favorable Changes in Food Preferences After Bariatric Surgery. Obes Surg, 31(8), 3514–3524. doi: 10.1007/s11695-021-05374-1 [DOI] [PubMed] [Google Scholar]
  34. Nielsen MS, Schmidt JB, le Roux CW, & Sjodin A (2019). Effects of Roux-en-Y Gastric Bypass and Sleeve Gastrectomy on Food Preferences and Potential Mechanisms Involved. Curr Obes Rep, 8(3), 292–300. doi: 10.1007/s13679-019-00354-0 [DOI] [PubMed] [Google Scholar]
  35. Park CH, Nam S-J, Choi HS, Kim KO, Kim DH, Kim J-W, … Hyun YS (2019). Comparative efficacy of bariatric surgery in the treatment of morbid obesity and diabetes mellitus: a systematic review and network meta-analysis. Obesity Surgery, 29(7), 2180–2190. doi: 10.1007/s11695-019-03831-6 [DOI] [PubMed] [Google Scholar]
  36. Pepino MY, Bradley D, Eagon JC, Sullivan S, Abumrad NA, & Klein S (2014). Changes in taste perception and eating behavior after bariatric surgery-induced weight loss in women. Obesity, 22(5), E13–E20. doi: 10.1002/oby.20649 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pucci A, & Batterham R (2019). Mechanisms underlying the weight loss effects of RYGB and SG: similar, yet different. Journal of Endocrinological Investigation, 42(2), 117–128. doi: 10.1007/s40618-018-0892-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Raatz SK, Johnson LK, Caliquary A, King WC, Kalarchian MA, Devlin MJ, … Mitchell JE (2020). Reported nutrient intake over 7 years after Roux-en-Y gastric bypass in the Longitudinal Assessment of Bariatric Surgery-3 (LABS-3) psychosocial study. Surg Obes Relat Dis, 16(8), 1022–1029. doi: 10.1016/j.soard.2020.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Ratcliff MB, Zeller MH, Inge TH, Hrovat KB, & Modi AC (2014). Feasibility of ecological momentary assessment to characterize adolescent postoperative diet and activity patterns after weight loss surgery. Surgery for Obesity and Related Diseases, 10(4), 705–710. doi: 10.1016/j.soard.2014.01.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Sarwer DB, Wadden TA, Moore RH, Baker AW, Gibbons LM, Raper SE, & Williams NN (2008). Preoperative eating behavior, postoperative dietary adherence, and weight loss after gastric bypass surgery. Surgery for Obesity and Related Diseases, 4(5), 640–646. doi: 10.1016/j.soard.2008.04.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sherf Dagan S, Goldenshluger A, Globus I, Schweiger C, Kessler Y, Kowen Sandbank G, … Sinai T (2017). Nutritional Recommendations for Adult Bariatric Surgery Patients: Clinical Practice. Adv Nutr, 8(2), 382–394. doi: 10.3945/an.116.014258 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shiffman S, Stone AA, & Hufford MR (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. doi: 10.1146/annurev.clinpsy.3.022806.091415 [DOI] [PubMed] [Google Scholar]
  43. Shoar S, Naderan M, Shoar N, Modukuru VR, & Mahmoodzadeh H (2019). Alteration Pattern of Taste Perception After Bariatric Surgery: a Systematic Review of Four Taste Domains. Obes Surg, 29(5), 1542–1550. doi: 10.1007/s11695-019-03730-w [DOI] [PubMed] [Google Scholar]
  44. Stunkard AJ, & Messick S (1985). The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29(1), 71–83. doi: 10.1016/0022-3999(85)90010-8 [DOI] [PubMed] [Google Scholar]
  45. Thomas JG, Bond DS, Ryder BA, Leahey TM, Vithiananthan S, Roye GD, & Wing RR (2011). Ecological momentary assessment of recommended postoperative eating and activity behaviors. Surgery for Obesity and Related Diseases, 7(2), 206–212. doi: 10.1016/j.soard.2010.10.007 [DOI] [PubMed] [Google Scholar]
  46. Williams-Kerver GA, Steffen KJ, & Mitchell JE (2019). Eating pathology after bariatric surgery: an updated review of the recent literature. Current psychiatry reports, 21(9), 1–13. doi: 10.1007/s11920-019-1071-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Williams-Kerver GA, Steffen KJ, Smith KE, Cao L, Crosby RD, & Engel SG (2020). Negative affect and loss of control eating among bariatric surgery patients: an ecological momentary assessment pilot investigation. Obesity Surgery, 30(6), 2382–2387. doi: 10.1007/s11695-020-04503-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wong L, Zafari N, Churilov L, Stammers L, Price S, Ekinci EI, & Sumithran P (2020). Change in emotional eating after bariatric surgery: systematic review and meta-analysis. BJS open, 4(6), 995–1014. doi: 10.1002/bjs5.50318 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zakeri R, & Batterham RL (2018). Potential mechanisms underlying the effect of bariatric surgery on eating behaviour. Curr Opin Endocrinol Diabetes Obes, 25(1), 3–11. doi: 10.1097/MED.0000000000000379 [DOI] [PubMed] [Google Scholar]
  50. Zarshenas N, Tapsell LC, Neale EP, Batterham M, & Talbot ML (2020). The relationship between bariatric surgery and diet quality: a systematic review. Obesity Surgery, 30(5), 1768–1792. doi: 10.1007/s11695-020-04392-9 [DOI] [PubMed] [Google Scholar]

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