ABSTRACT
Purpose
Pre‐operative eating disorders are well documented within the metabolic and bariatric surgery (MBS) population, yet subthreshold dieting attempts are less understood. The objectives of this study were to define and categorize patients' preoperative dieting attempts, and to determine how attempts are associated with postoperative outcomes, eating disorders, and demographics.
Materials and Methods
Three hundred twenty‐one patients (81.0% female; 68.3% White) who had MBS (57.3% Roux‐en‐Y) between 2019 and 2020 were included. Preoperative dieting attempt responses were categorized as provider‐managed, non‐medically managed, and self‐directed attempts; subtypes of dieting methods (e.g., low calorie) were described. Descriptive analyses were conducted for attempt categories and subtypes, and between attempts and readmissions, complications, eating disorders, and demographics. ANOVAs determined associations between attempts and %TWL at 6 and 12 months.
Results
Patients reported an average of five to six preoperative dieting attempts; self‐directed attempts were the most common (91.9%), and exercise was the most common subcategory (70.7%). Patients with ≥ 1 provider‐managed attempt were less likely to experience a complication (p < 0.001) and more likely to experience readmission (p = 0.018). Patients with 1 self‐directed attempt were less likely to experience a complication (p = 0.045) and readmission (p < 0.001). Patients who experienced ≥ 2 low fat diet attempts were more likely to have complications (p < 0.001) and readmissions (p = 0.008); patients with ≥ 2 VLCD attempts were more likely to have a complication (p < 0.001). Patients who experienced ≥ 2 non‐medically managed attempts had higher preoperative BMIs (p = 0.03).
Discussion
Given that patients engaged in frequent dieting attempts that fall outside formal assessments, future work should seek to expand pre‐operative assessments.
Keywords: dieting behaviors, disordered eating, metabolic and bariatric surgery, weight loss
1. Introduction
Metabolic and bariatric surgery (MBS) is the most effective method of achieving sustainable long‐term weight loss, particularly when diet, exercise, and drug therapy have not been successful [1]. Primary mechanisms of weight loss across procedures include inducing satiety, reducing hunger, and impeding the ability to overconsume [2]. In combination, these factors promote a shift in postoperative eating behaviors that can lead to significant weight loss [3]. However, patients with MBS are more likely to have disordered eating [4, 5, 6], heightened reactivity around food [7], and dysregulated hunger and satiety cues [8] than the general population, all of which can interfere with the success of the primary weight loss mechanisms associated with surgery. There is evidence that patients with these types of postoperative eating patterns have suboptimal weight loss [9, 10], a need for revisional surgery [11], and poor psychosocial outcomes [12].
The most common type of preoperative diets recommended to patients are energy‐restricted diets, in which the weight loss market is currently valued at $75 billion USD [13]. Energy‐restriction diets have been associated with poor long‐term weight management outcomes [14, 15, 16], such as weight regain [17], effectively creating “repeat customers.” These diets have been linked to the development of uncontrollable food cravings [18, 19], binge eating [20], emotional eating [21], malnourishment [22], and eating disturbances [23], all of which decrease the success of future attempts at eating behavior change. Additionally, these types of diets often position eating behaviors in ways that increase the likelihood of developing disordered eating, anxiety around food consumption, and increase the internalization of weight stigma [24].
Patients pursuing MBS are typically assessed for diagnosable eating disorder (ED) pathology, particularly for binge eating disorder and bulimia nervosa, as part of a preoperative psychological evaluation [10]. Some providers also collect information about patients' previous preoperative attempts at dieting, weight loss and management, although these reports are infrequently considered as contributing to or part of possible disordered eating [4, 5] or of importance when considering possible impact on postoperative outcomes. Although the effects of binge eating disorder and emotional eating pathology on postoperative outcomes are well‐documented, little is known about subthreshold and general eating pathology connected to a history of dieting attempts, which may seem unproblematic at first glance even to trained providers [25, 26].
The majority of literature in this area details specific types of dieting or weight loss attempts that were unrelated to postoperative weight loss. Overall, patients endorsed using self‐directed and commercial dieting programs more often than medically‐supervised or anti‐obesity medications [27, 28]. Researchers have also noted no effects from preoperative dieting and weight loss attempts on long term postoperative weight loss [27, 29]. For example, Deb and colleagues [27] reported that neither the total number of attempts nor the total duration of attempted weight loss methods in years had any effect on preoperative BMI or weight loss at 12 month postsurgery. Other researchers have noted that requiring medically‐managed preoperative weight loss attempts may not be helpful and instead needlessly inhibit access to surgery [30, 31, 32].
The primary objective of this study was to define and categorize the types of patients' self‐reported preoperative weight loss and dieting (referred to as “dieting” herein) attempts, updated to reflect recent dieting and weight loss phenomena. The secondary objective was to determine how dieting attempts associated with current and previously diagnosed EDs (Binge Eating Disorder, Unspecified Eating Disorder), postoperative 3‐month readmissions, 30‐day complications, and weight loss over 12 months were assessed, including trends in patient demographics. An examination of the comprehensive diet and exercise histories of patients seeking MBS will provide novel information about the seemingly innocuous preoperative dieting behaviors that may need to be addressed to increase the chances of successful postoperative outcomes.
2. Methods
2.1. Study Design
The current study was a secondary data analysis from a descriptive study examining preoperative psychiatric and behavioral predictors of MBS outcomes from electronic health record data [4, 5]. The prior studies focused on associations between patient demographics and DSM diagnoses from the pre‐operative psychological evaluation outcomes, follow‐up rates with the psychologist, and postoperative outcomes. This was the first study using this data to examine previous dieting attempts and disordered eating behaviors.
2.2. Procedures
During the pre‐operative psychological evaluation, patients completed a clinical interview assessing for psychiatric, ED, and substance use diagnoses. Patients were seen for their initial evaluation between August 2019 and December 2020. The original sample included 508 participants. 187 participants were excluded from this study according to the following criteria: not receiving MBS at the clinic for any reason despite completing the evaluation (n = 186) and not having any readmission data up to 3 months post‐surgery (n = 1). The remaining participants (N = 321) represented 63.2% of the original sample.
The final analytic sample was composed of 321 patients who had completed their preoperative psychological evaluation during this timeframe with a singular psychologist, had %TWL data available for at least one visit post‐surgery, had readmission data available for 3 months post‐surgery, were ≥ 18‐years‐old, and had a BMI ≥ 35 with weight‐related medical problems or ≥ 40 with no comorbidity [33]. Patient demographics, current and historical diagnoses, and postoperative outcomes were extracted from the electronic health record. All data collection procedures were approved by the Institutional Review Board of The Ohio State University.
2.3. Measures
2.3.1. Clinical Demographics
Demographics included patient age, sex, highest educational attainment, employment status, insurance type, and marital status at the time of the psychological evaluation. Participants received Roux‐en‐Y gastric bypass (57.3%) or sleeve gastrectomy (42.7%). All past or current psychiatric diagnoses were coded by category (yes, no). ED diagnoses included any (19.7% of all patients), binge eating disorder (6.1% of all patients), and unspecified eating or feeding disorder (9.4% of all patients). During the pre‐operative psychological evaluation, patients met with a single psychologist to complete a clinical interview and assessments in which eating disorders and disordered eating were assessed based on DSM–5 criteria.
2.3.2. Anthropometrics
Patient weight, height, and BMI were extracted from the pre‐operative psychological evaluation, at the date of surgery, and postoperative clinical visits (2‐ or 3‐, 6‐, and 12‐month). Change in BMI (ΔBMI) and %TWL were calculated from the date of surgery through each follow‐up [34]. Any postoperative readmissions within 3 months and 30‐day complications were obtained from the electronic health record, coded as yes or no.
2.3.3. Patient Reported Dieting Attempts
Patients were asked open‐ended questions by the psychologist regarding previous methods attempted for weight loss throughout their lifetime, regardless of whether initial and/or sustained weight loss was achieved. Patients' self‐reported preoperative dieting attempts were coded into categorical variables including main categories (n = 3) and sub‐categories (n = 9) by the authors using a codebook.
The main three categories included: provider‐managed attempts, which included subcategories (1) prescription weight loss medications, (2) physician‐managed diets or programs, (3) dietitian‐managed diet or program; non‐medically managed attempts, included subcategories (4) over‐the‐counter medications and supplements, (5) commercial diet programs, and (6) fad or specialty diets; and self‐directed attempts, which included the subcategory (7) self‐monitoring of caloric or macro intake, (8) self‐directed dieting, and (9) exercise. These categories were similar (although not identical) to those used by Gibbons and colleagues [28] to describe types of dieting attempts. Each main category and sub‐category variable were coded for 0, 1, or ≥ 2 attempts. The most frequent patient reported examples were noted for applicable sub‐categories (i.e., prescription medication, over‐the‐counter medications or supplements, commercial diet programs, fad and specialty diets, self‐monitoring, and self‐directed diets).
Dieting attempts were additionally separately coded for 8 subtypes based on defining characteristics. Each dieting attempt named in the dataset was cross‐checked for information on the appropriate commercial, product, or organization website. Daily caloric and macronutrient intakes were evaluated only if they were explicitly offered on a website or in associated company materials. All dieting attempts not explicitly associated with a commercial program or trademarked product (e.g., “apple cider vinegar shots”) did not receive a label due to the lack of appropriate reference information. All standards for what constituted “high” or “low” daily intake values were created based on nutritional Daily Reference Values and acceptable caloric and macronutrient distribution ranges determined by the National Institutes of Health [35]. The cutoff for high protein diets was not calculated in grams as the Daily Reference Values for protein are based on individual weight.
Based on the above criteria, diets recommending the consumption of 1200–1800 kcal/day were labeled as (1) low calorie diets and (2) < 1200 kcal/day were labeled very low calorie diets (VLCD). Diets recommending < 100 g carbs/day or < 40% of daily total intake (DTI) were labeled as (3) low carb diets; < 80 g fat/day or < 25% of DTI were labeled as (4) low fat diet, whereas ≥ 120 g fat/day or ≥ 35% of DTI were labeled as (5) high fat diet; and ≥ 200 g protein/day or ≥ 35% DTI were labeled as (6) high protein diet. Two additional labels were applied: attempts were labeled (7) appetite suppressant for prescription medications, over‐the‐counter supplements, and injections that purport to suppress appetite as the primary mechanism of weight loss; and (8) eating disorder behavior for instances where disordered eating as a form of weight management was directly labeled by the patient (e.g., “abusing laxatives”). Each subtype variable was coded for 0, 1, or ≥ 2 attempts.
2.4. Analysis
To determine the types of previous dieting attempts, descriptive statistics were conducted for each type of category and subtype (frequency, percentage). Only the subtypes of previous dieting attempts with at least 30% prevalence in the sample were utilized (low calorie diet, low carb diet, very low calorie, high protein diet, high fat diet, appetite suppressant) in the analyses. Chi‐square analyses determined associations between current and past ED diagnosis with type of dieting attempt. Chi‐square analyses also determined associations between type of previous dieting attempt with 3 months readmissions and 30‐day complications. ANOVAs were conducted to determine associations between type of dieting attempts with %TWL at 2 or 3 months (depending on procedure), 6 months, and 12 months. To determine associations between patient demographics and dieting attempts, a series of Chi‐square tests (race, ethnicity, sex, insurance, marital status, education, employment) and independent t‐tests (age) were conducted depending on the nature of the demographic variables. The criterion for significance was set at p < 0.05. All analyses were conducted using SPSS Version 28 (IBM). Missing %TWL or BMI data at follow‐up indicated that patients did not complete the visit.
3. Results
3.1. Clinical Demographics
The clinical demographics of the sample are shown in Table 1. The average age of patients at surgery was 41 years with a BMI of 48.65 ± 8.39 at the time of their psychological intake. The majority of the sample identified as female (81.0%), White (64.1%), partially or fully employed (72.9%), with above a high school diploma or GED (76.0%) and received private health insurance (60.5%). The sample was split between patients who identified as married or cohabitating (55.8%) and single or widowed/divorced/separated (44.3%), and who received the Roux‐en‐Y Gastric Bypass (57.3%) versus the Sleeve Gastrectomy (42.7%) surgical procedure. Few patients experienced one or more readmissions (7.5%) within three months post‐surgery or had at least one 30‐day complication (29.8%).
TABLE 1.
Clinical demographics and outcomes N = 321.
| Age, in years | 41.35 ± 10.57, 21.00–73.00 |
| Sex | |
| Male | 61 (19.0) |
| Female | 260 (81.0) |
| Race (n = 320) | |
| White | 205 (64.1) |
| Black | 95 (29.7) |
| Other or multiple races | 20 (6.2) |
| Insurance (n = 311) | |
| Public | 123 (39.5) |
| Private | 188 (60.5) |
| Employment | |
| Full/partial | 234 (72.9) |
| Not employed | 87 (27.1) |
| Education | |
| ≤ High school diploma/GED | 77 (24.0) |
| ≤ Associate/bachelor's degree | 205 (63.9) |
| ≥ Graduate degree | 39 (12.1) |
| Marital status | |
| Married/cohabitating | 179 (55.8) |
| Never married/single | 85 (26.5) |
| Widowed/divorced/separated | 57 (17.8) |
| Living alone | |
| Yes | 37 (11.5) |
| No | 284 (88.5) |
| Surgical procedure | |
| Roux‐en‐Y gastric bypass | 184 (57.3) |
| Sleeve gastrectomy | 137 (42.7) |
| Procedure delayed | |
| Yes | 91 (28.3) |
| No | 230 (71.7) |
| Weight–Intake, in kgs | 137.82 ± 29.31, 83.46–296.65 |
| BMI–Intake | 48.65 ± 8.39, 34.90–90.50 |
| Weight–Surgery, in kgs | 133.39 ± 26.35, 84.14–269.89 |
| BMI–Surgery | 47.28 ± 7.71, 30.69–85.19 |
| ΔBMI–3 Months (n = 115) | 7.91 ± 2.82,‐3.31–21.48 |
| %TWL–3 Months (n = 115) | 16.37 ± 4.68,‐6.68–30.67 |
| ΔBMI–6 Months (n = 215) | 10.94 ± 3.49,‐0.74–24.28 |
| %TWL–6 Months (n = 215) | 23.16 ± 6.03,‐4.64–41.60 |
| ΔBMI–12 Months (n = 111) | 13.26 ± 5.17,‐2.24–35.07 |
| %TWL–12 Months (n = 110) | 28.16 ± 8.78,‐6.75–47.44 |
| Readmissions–3 Months (n = 320) | |
| 0 | 296 (92.5) |
| ≥ 1 | 24 (7.5) |
| 30 Day complications–3 Month | |
| 0 | 225 (70.1) |
| 1 | 65 (20.2) |
| ≥ 2 | 31 (9.6) |
3.2. Preoperative Dieting Attempts
Information about patients' preoperative dieting attempts is in Table 2. Patients reported average 5.39 ± 2.07 preoperative dieting attempts. Of the main three categories, 66.1% of patients reported at least one provider‐managed attempt, 85.1% reported at least one non‐medically managed attempt, and 91.9% reported at least one self‐directed attempt.
TABLE 2.
Descriptions of previous dieting attempt categories, subcategories, examples, and dieting method subtypes (N = 321).
| Categories and subcategories | Attempts | Number (%) | Patient‐reported examples | Number (%) |
|---|---|---|---|---|
| 1. Provider‐managed attempts | 0 | 109 (34.0) | ||
| 1 | 137 (42.7) | |||
| ≥ 2 | 75 (23.4) | |||
| 1a. Prescription medication | 0 | 177 (55.1) | Phentermine (any formulation) | 112 (34.9) |
| 1 | 105 (32.7) | Bupropion (any formulation) | 10 (3.1) | |
| ≥ 2 | 39 (12.1) | Other | 45 (14.0) | |
| 1b. Physician‐supervised diets/Programs | 0 | 290 (90.3) | ||
| ≥ 1 | 31 (9.6) | |||
| 1c. Registered dietitian/Nutritionist (RDN) consults | 0 | 227 (70.7) | ||
| ≥ 1 | 94 (29.3) | |||
| 2. Non‐medically‐managed attempts | 0 | 48 (15.0) | ||
| 1 | 77 (24.0) | |||
| ≥ 2 | 196 (61.1) | |||
| 2a. Over‐the‐counter (OTC) medications/Supplements | 0 | 247 (76.9) | Hydroxycut | 16 (5.0) |
| ≥ 1 | 74 (23.1) | Other | 66 (20.6) | |
| 2b. Commercial diet programs | 0 | 110 (34.3) | Weight watchers | 136 (42.4) |
| 1 | 129 (40.2) | Slimfast | 52 (16.2) | |
| ≥ 2 | 82 (25.5) | Other | 147 (45.8) | |
| 2c. Fad and specialty diet programs | 0 | 142 (44.2) | Keto | 100 (31.2) |
| 1 | 119 (37.1) | Liquid diet/Cleanse/Meal replacements | 46 (14.3) | |
| ≥ 2 | 60 (18.7) | Other | 109 (34.0) | |
| 3. Self‐directed attempts | 0 | 26 (8.1) | ||
| 1 | 79 (24.6) | |||
| ≥ 2 | 216 (67.3) | |||
| 3a. Self‐monitoring | 0 | 195 (60.7) | Counting calories/Macros | 54 (16.8) |
| ≥ 1 | 126 (39.3) | Other | 88 (27.4) | |
| 3b. Self‐directed diets | 0 | 135 (42.1) | Reducing carbohydrate intake | 72 (22.4) |
| 1 | 90 (28.0) | Reducing caloric intake | 45 (14.0) | |
| ≥ 2 | 96 (30.0) | Other | 65 (59.1) | |
| 3c. Exercise | 0 | 94 (29.3) | ||
| ≥ 1 | 227 (70.7) | |||
| Subtypes | Attempts | Number (%) | Explanation |
|---|---|---|---|
| Low calorie diets | 0 | 57 (17.8) | 1200–1800 kcal/day (most often ≤ 1500 kcal/day), regardless of medical supervision |
| 1 | 125 (38.9) | ||
| ≥ 2 | 139 (43.3) | ||
| Very low calorie diets (VLCDs) | 0 | 192 (59.8) | < 1200 kcal/day (most often ≤ 800 kcal/day), regardless of medical supervision |
| 1 | 115 (35.8) | ||
| ≥ 2 | 14 (4.4) | ||
| Low carb diets | 0 | 137 (42.7) | < 100 g carbs/day, or < 40% of total daily intake |
| 1 | 119 (37.1) | ||
| ≥ 2 | 65 (20.2) | ||
| Low fat diets | 0 | 267 (83.2) | < 80 g fat/day, or < 25% of total daily intake |
| 1 | 54 (16.8) | ||
| High fat diets | 0 | 216 (67.3) | ≥ 120 g fat/day, or ≥ 35% of total daily intake |
| ≥ 1 | 105 (32.7) | ||
| High protein diets | 0 | 209 (65.1) | ≥ 200 g protein/day, or ≥ 35% of total daily intake |
| ≥ 1 | 112 (34.9) | ||
| Appetite suppressants | 0 | 168 (52.3) | Prescription medication, over‐the‐counter pill, or injection that purports to suppress appetite as the primary mode of weight loss/management |
| 1 | 102 (31.8) | ||
| ≥ 2 | 51 (15.9) | ||
| Disordered eating | 0 | 311 (96.9) | Directly expressed method of disordered eating (i.e., “abuse of laxatives”) |
| ≥ 1 | 10 (3.1) |
*p < 0.05; **p < 0.001.
Within the category of provider‐managed attempts, the most frequently reported sub‐category was prescription weight loss medications (44.8% reporting at least one), with phentermine as the most common medication (34.9% of all participants). Within the category of non‐medically managed attempts, commercial dieting programs were the most frequent sub‐category reported (65.7% of patients reporting at least one), with the most frequently reported being Weight Watchers (42.4%) and a ketogenic diet (31.2%). This was followed by closely by fad and specialty diet programs at 55.8%. Within the category of self‐directed attempts, the sub‐category of exercise was the most commonly reported (70.7%).
Table 2 further breaks down the three main categories into nine subtypes based on characteristics specific to the diet, medication, or over‐the‐counter pill/supplement, and when applicable provides top examples reported by patients. The most common subtypes of diet were low calorie diets (82.2%), low carb diets (57.3%), appetite suppressants (47.7%), and VLCDs (40.2%). Notably, it was more common for participants to report ≥ 2 low calorie diet attempts (43.3%) than 1 attempt only (38.9%). Although somewhat less frequent, at least 1 attempt of high protein diets (34.9%) and high fat diets (32.7%) for dieting was still reported by over a third of participants. Most participants did not report direct disordered eating behaviors outside the eating disorder assessment portion of the pre‐operative psychological evaluation (96.9%).
3.3. Dieting Attempts, Methods Subtypes, and Postoperative Outcomes
Tables 3 and 4 contain the analyses of dieting attempts (categories and subtypes) with postoperative readmissions and complications. Patients who experienced no provider‐managed attempts (6.4% readmission rate) were less likely to have a postoperative readmission compared to patients with 1 (8.1%) or ≥ 2 (8.0%) provider‐managed attempts (X 2[15] = 28.668, p = 0.018). Additionally, patients who experienced 1 self‐directed attempt were less likely to have a postoperative readmission (6.3% vs. 7.7%) compared to no self‐directed attempts or ≥ 2 self‐directed attempts (7.9%; X 2[18] = 61.762, p < 0.001).
TABLE 3.
Dieting attempt categories and readmissions and complications.
| No Readmissions | ≥ 1 Readmissions | |
|---|---|---|
| Provider attempts* | ||
| 0 | 102 | 7 |
| 1 | 125 | 11 |
| ≥ 2 | 69 | 6 |
| Non‐medically managed attempts | ||
| 0 | 45 | 3 |
| 1 | 72 | 5 |
| ≥ 2 | 179 | 16 |
| Self‐directed attempts** | ||
| 0 | 24 | 2 |
| 1 | 74 | 5 |
| ≥ 2 | 198 | 17 |
| No 30‐Day Complications | ≥ 1 30‐Day Complication | |
|---|---|---|
| Provider attempts* | ||
| 0 | 76 | 33 |
| 1 | 104 | 33 |
| ≥ 2 | 45 | 30 |
| Non‐medically managed attempts** | ||
| 0 | 28 | 20 |
| 1 | 54 | 23 |
| ≥ 2 | 143 | 53 |
| Self‐directed attempts* | ||
| 0 | 18 | 8 |
| 1 | 60 | 19 |
| ≥ 2 | 147 | 69 |
TABLE 4.
Dieting subtypes and readmissions and complications.
| No Readmissions | ≥ 1 Readmissions | |
|---|---|---|
| Very low calorie diets | ||
| 0 | 176 | 16 |
| 1 | 107 | 7 |
| ≥ 2 | 13 | 1 |
| Low calorie diets | ||
| 0 | 53 | 4 |
| 1 | 118 | 6 |
| ≥ 2 | 125 | 14 |
| Low carb diets | ||
| 0 | 126 | 11 |
| 1 | 110 | 9 |
| ≥ 2 | 60 | 4 |
| Low fat diets* | ||
| 0 | 248 | 18 |
| 1 | 41 | 5 |
| ≥ 2 | 7 | 1 |
| High fat diets | ||
| 0 | 198 | 17 |
| 1 | 95 | 7 |
| ≥ 2 | 3 | 0 |
| High protein diets | ||
| 0 | 191 | 17 |
| 1 | 100 | 6 |
| ≥ 2 | 5 | 1 |
| Appetite suppressants | ||
| 0 | 155 | 12 |
| 1 | 96 | 6 |
| ≥ 2 | 45 | 6 |
| Disordered eating behaviors | ||
| 0 | 286 | 24 |
| 1 | 8 | 0 |
| ≥ 2 | 2 | 0 |
| No 30‐Day Complications | ≥ 1 30‐Day Complication | |
|---|---|---|
| Very low calorie diets** | ||
| 0 | 131 | 61 |
| 1 | 87 | 28 |
| ≥ 2 | 7 | 7 |
| Low calorie diets | ||
| 0 | 38 | 19 |
| 1 | 87 | 38 |
| ≥ 2 | 100 | 39 |
| Low carb diets | ||
| 0 | 88 | 49 |
| 1 | 87 | 32 |
| ≥ 2 | 50 | 15 |
| Low fat diets** | ||
| 0 | 183 | 84 |
| 1 | 36 | 10 |
| ≥ 2 | 6 | 2 |
| High fat diets | ||
| 0 | 148 | 68 |
| 1 | 74 | 28 |
| ≥ 2 | 3 | 0 |
| High protein diets | ||
| 0 | 142 | 67 |
| 1 | 79 | 27 |
| ≥ 2 | 4 | 2 |
| Appetite suppressants | ||
| 0 | 121 | 47 |
| 1 | 71 | 31 |
| ≥ 2 | 33 | 18 |
| Disordered eating behaviors | ||
| 0 | 218 | 93 |
| 1 | 5 | 3 |
| ≥ 2 | 2 | 0 |
*p < 0.05; **p < 0.001.
Patients who experienced 1 provider‐managed attempt (24.1%) were less likely to have a 30‐day complication compared to patients who did not experience a provider‐managed attempt (30.3%) or ≥ 2 provider‐managed attempts (40%; X 2[25] = 38.258, p = 0.044). Similarly, patients who experienced 1 self‐directed attempt (24.1%) were less likely to have a complication compared with those who experienced no self‐direct attempts or ≥ 2 attempts (30.8% and 31.9%, respectively; X 2[10] = 18.671, p = 0.045). Patients who experienced no non‐medically managed attempts (41.7%) were more likely to have complications compared to patients with 1 attempt (29.9%) or ≥ 2 attempts (27.0%; X 2[45] = 104.160, p < 0.001).
For specific subtypes, patients who experienced ≥ 2 low fat diet attempts were more likely to have 30‐day complications and readmissions (complications: 25.0%; readmissions: 12.5%) compared to patients reporting only 1 attempt (21.7%; 10.9%) or no attempts (31.5%; 6.8%) (complications: X 2[20] = 95.736, p < 0.001; readmissions: X 2[4] = 13.860, p = 0.008). For VLCDs, patients who experienced ≥ 2 attempts were more likely to have 30‐day complications (50.0%) than patients who only experienced 1 attempt (24.3%) or no attempts (31.8%; X 2[10] = 28.730, p < 0.001).
Table 5 displays the results of the ANOVA analysis of dieting attempt categories and %TWL. The only significant result was between non‐medically managed attempts and BMI at the psychological evaluation, in which patients who experienced ≥ 2 non‐medically managed attempts had higher BMIs compared to those who experienced 0 or 1 non‐medically managed attempts (F[2, 318] = 3.44, p = 0.03). A post hoc comparison using the Tukey HSD test indicated that the mean BMI for patients who had 1 non‐medically managed attempt (50.81 ± 9.13) was significantly higher compared to patients with ≥ 2 non‐medically managed attempts (48.02 ± 7.94); there was no significant difference for those with 0 non‐medically managed attempts.
TABLE 5.
Dieting attempt categories and %TWL at 6 and 12 Months.
| BMI at Psychological Evaluation (n = 321) | M ± SD | n |
|---|---|---|
| Provider attempts | ||
| 0 | 48.73 ± 8.89 | 109 |
| 1 | 48.59 ± 7.72 | 137 |
| ≥ 2 | 48.63 ± 8.91 | 75 |
| Non‐medically managed attempts * | ||
| 0 | 47.74 ± 8.52 | 48 |
| 1 | 50.81 ± 9.13 | 77 |
| ≥ 2 | 48.02 ± 7.94 | 196 |
| Self‐directed attempts | ||
| 0 | 46.31 ± 6.67 | 26 |
| 1 | 48.56 ± 7.53 | 76 |
| ≥ 2 | 48.96 ± 8.84 | 216 |
| 6‐month %TWL (n = 215) | M ± SD | n |
|---|---|---|
| Provider attempts | ||
| 0 | 23.58 ± 6.46 | 76 |
| 1 | 23.14 ± 6.37 | 89 |
| ≥ 2 | 22.53 ± 4.63 | 50 |
| Non‐medically managed attempts | ||
| 0 | 22.23 ± 5.34 | 36 |
| 1 | 22.80 ± 6.58 | 49 |
| ≥ 2 | 23.55 ± 6.01 | 130 |
| Self‐directed attempts | ||
| 0 | 24.68 ± 5.90 | 20 |
| 1 | 22.74 ± 6.00 | 54 |
| ≥ 2 | 23.10 ± 6.07 | 141 |
| 12‐month %TWL (n = 110) | M ± SD | N |
|---|---|---|
| Provider attempts | ||
| 0 | 28.49 ± 10.03 | 38 |
| 1 | 28.74 ± 8.14 | 41 |
| ≥ 2 | 27.00 ± 8.11 | 31 |
| Non‐medically managed attempts | ||
| 0 | 25.69 ± 8.76 | 18 |
| 1 | 30.50 ± 10.08 | 27 |
| ≥ 2 | 27.88 ± 8.10 | 65 |
| Self‐directed attempts | ||
| 0 | 29.26 ± 8.84 | 10 |
| 1 | 27.70 ± 9.77 | 29 |
| ≥ 2 | 28.20 ± 8.46 | 71 |
*p < 0.05; **p < 0.001.
3.4. Dieting Attempts and Eating Disorder Diagnoses
There were no significant results based on historical or current ED diagnoses with readmissions or 30‐day complications (see Table 7).
TABLE 7.
Eating disorders and readmissions, ER visits, and complications.
| No Readmissions | ≥ 1 Readmissions | n | X 2 (df) | p | |
|---|---|---|---|---|---|
| Binge eating disorder | |||||
| No | 272 | 22 | 294 | 0.746(3) | 0.862 |
| Yes | 24 | 4 | 26 | ||
| Any other eating disorder | |||||
| No | 274 | 22 | 296 | 0.827(3) | 0.843 |
| Yes | 22 | 2 | 24 | ||
| No ER Visits | ≥ 1 ER Visits | n | X 2 (df) | p | |
|---|---|---|---|---|---|
| Binge eating disorder | |||||
| No | 246 | 48 | 294 | 1.121(4) | 0.891 |
| Yes | 22 | 4 | 26 | ||
| Any other eating disorder | |||||
| No | 247 | 49 | 296 | 0.937(4) | 0.919 |
| Yes | 21 | 3 | 24 | ||
| No 30‐Day Complications | ≥ 1 30‐Day Complications | n | X 2 (df) | p | |
|---|---|---|---|---|---|
| Binge eating disorder | |||||
| No | 209 | 86 | 295 | 4.370(5) | 0.497 |
| Yes | 16 | 10 | 26 | ||
| Any other eating disorder | |||||
| No | 210 | 87 | 297 | 1870(5) | 0.867 |
| Yes | 15 | 9 | 24 | ||
3.5. Dieting Attempts and Clinical Demographics
There were no significant associations based on participant sex, race, and marital status with dieting attempt categories (see Table 6). Participants who were partially or fully employed were more likely to have utilized provider‐managed attempts (47.0% with one attempt, 24.4% with ≥ 2 attempts) compared with participants who were not currently employed (31.0% with one attempt, 20.7% with ≥ 2 attempts) (X 2[2] = 11.364, p = 0.003). Participants with public health insurance were more likely to have ≥ 2 non‐medically managed attempts (67.0% vs. 50.4% for private health insurance), as well as participants who were currently employed (65.9% vs. 48.3%), received at least one college degree (61.5% for associates/bachelor's and 82.1% for graduate degree, vs. 49.4% for high school diploma/GED only), and were not currently living alone (62.3% vs. 51.4%) (insurance: X 2 [2] = 12.493, p = 0.002; employment: X 2 [20] = 8.483, p = 0.014; education: X 2 [4] = 14.294, p = 0.006; living alone: X 2 [9] = 21.835, p = 0.009). For self‐directed attempts, participants who received the sleeve gastrectomy reported ≥ 2 attempts more often (73.7%) than patients who received Roux‐en‐Y gastric bypass (62.5%) (X 2 [2] = 6.483, p = 0.039).
TABLE 6.
Dieting attempt categories and clinical demographics.
| 0 Provider Attempts | 1 Provider Attempt | ≥ 2 Provider Attempts | n | |
|---|---|---|---|---|
| Sex | ||||
| Male | 22 | 28 | 11 | 61 |
| Female | 87 | 109 | 64 | 260 |
| Race | ||||
| White | 73 | 85 | 47 | 205 |
| Black | 30 | 44 | 21 | 95 |
| Other or multiple races | 6 | 8 | 6 | 20 |
| Insurance | ||||
| Public | 64 | 84 | 40 | 188 |
| Private | 44 | 49 | 30 | 123 |
| Employment | ||||
| Full/Partial* | 67 | 110 | 57 | 234 |
| Not employed | 42 | 27 | 18 | 87 |
| Education | ||||
| ≤ High school Diploma/GED | 27 | 35 | 15 | 77 |
| ≤ Associate/Bachelor's degree | 68 | 88 | 49 | 205 |
| ≥ Graduate degree | 14 | 14 | 11 | 39 |
| Marital status | ||||
| Married/Cohabitating | 63 | 73 | 43 | 179 |
| Widowed/Divorced/Separated | 20 | 21 | 16 | 57 |
| Never married/Single | 26 | 43 | 16 | 85 |
| Living alone | ||||
| Yes | 14 | 14 | 9 | 37 |
| No | 95 | 123 | 66 | 284 |
| Surgical procedure | ||||
| Roux‐en‐Y gastric bypass | 65 | 73 | 46 | 184 |
| Sleeve gastrectomy | 44 | 64 | 29 | 137 |
| Procedure delayed | ||||
| Yes | 35 | 36 | 20 | 91 |
| No | 74 | 101 | 55 | 230 |
| 0 Non‐Medically Managed Attempts | 1 Non‐Medically Managed Attempt | ≥ 2 Non‐Medically Managed Attempts | n | |
|---|---|---|---|---|
| Sex | ||||
| Male | 14 | 16 | 31 | 61 |
| Female | 34 | 61 | 165 | 260 |
| Race | ||||
| White | 28 | 44 | 133 | 205 |
| Black | 16 | 27 | 52 | 95 |
| Other or multiple races | 4 | 6 | 10 | 20 |
| Insurance | ||||
| Public* | 18 | 44 | 126 | 188 |
| Private | 28 | 33 | 62 | 123 |
| Employment | ||||
| Full/Partial* | 32 | 48 | 154 | 234 |
| Not employed | 16 | 29 | 42 | 87 |
| Education | ||||
| ≤ High school Diploma/GED* | 17 | 22 | 38 | 77 |
| ≤ Associate/Bachelor's degree | 31 | 48 | 126 | 205 |
| ≥ Graduate degree | 0 | 7 | 32 | 39 |
| Marital status | ||||
| Married/Cohabitating | 23 | 43 | 113 | 179 |
| Widowed/Divorced/Separated | 10 | 12 | 35 | 57 |
| Never married/Single | 15 | 22 | 48 | 85 |
| Living alone | ||||
| Yes* | 11 | 7 | 19 | 37 |
| No | 37 | 70 | 177 | 284 |
| Surgical procedure | ||||
| Roux‐en‐Y gastric bypass | 31 | 41 | 112 | 184 |
| Sleeve gastrectomy | 17 | 36 | 84 | 137 |
| Procedure delayed | ||||
| Yes | 16 | 23 | 52 | 91 |
| No | 32 | 54 | 144 | 230 |
| 0 Self‐Directed Attempts | 1 Self‐Directed Attempt | ≥ 2 Self‐Directed Attempts | n | |
|---|---|---|---|---|
| Sex | ||||
| Male | 2 | 14 | 61 | 61 |
| Female | 24 | 65 | 171 | 260 |
| Race | ||||
| White | 19 | 50 | 136 | 205 |
| Black | 6 | 21 | 68 | 95 |
| Other or multiple races | 1 | 7 | 12 | 20 |
| Insurance | ||||
| Public | 17 | 49 | 122 | 188 |
| Private | 8 | 27 | 88 | 123 |
| Employment | ||||
| Full/Partial | 19 | 56 | 159 | 234 |
| Not employed | 7 | 23 | 57 | 87 |
| Education | ||||
| ≤ High school Diploma/GED | 7 | 20 | 50 | 77 |
| ≤ Associate/Bachelor's degree | 15 | 47 | 143 | 205 |
| ≥ Graduate degree | 4 | 12 | 23 | 39 |
| Marital status | ||||
| Married/Cohabitating | 14 | 46 | 119 | 179 |
| Widowed/Divorced/Separated | 6 | 15 | 36 | 57 |
| Never married/Single | 6 | 18 | 61 | 85 |
| Living alone | ||||
| Yes | 2 | 9 | 26 | 37 |
| No | 24 | 70 | 190 | 284 |
| Surgical procedure | ||||
| Roux‐en‐Y gastric bypass* | 14 | 55 | 115 | 184 |
| Sleeve gastrectomy | 12 | 24 | 101 | 137 |
| Procedure delayed | ||||
| Yes | 5 | 23 | 63 | 91 |
| No | 21 | 56 | 153 | 230 |
*p < 0.05; **p < 0.001.
4. Discussion
Our findings show that before undergoing MBS, patients reported an average of five to six previous attempts at dieting, which was slightly higher than previously reported [27]. Self‐directed dieting attempts were the most frequently reported category, followed by non‐medically managed attempts. Provider attempts, while the least frequent reported category, were still reported by over three in five patients, which is consistent with previous literature [27, 28]. This study is among the first to explore and define specific categories and types of preoperative attempts at dieting that fall outside formal eating disorder diagnoses and assessments. While prior literature has focused on the identification of preoperative EDs and disordered eating behaviors, the unidentified types of dieting attempts that patients have pursued prior to surgery may have negative effects on postoperative outcomes.
Many of the types of attempts were not significantly associated with postoperative outcomes, which was also consistent with previous literature [27, 28, 29, 30, 31]. This suggests that, despite shifts in available weight loss and dieting methods over the past 20 years, their associations with MBS patient outcomes do not reach statistical significance. However, the broader categories (non‐medically managed, provider‐managed, self‐directed dieting attempts) yielded some important information about what types of attempts are important for consideration beyond simply evaluating direct disordered eating behaviors. Although research has focused on associations between patients' preoperative high energy intake and postoperative outcomes, this study demonstrated complex associations between preoperative attempts at low or reduced energy intake and postoperative outcomes. Findings that patients who reported one or more provider‐managed or two or more self‐directed attempts were more likely to have postoperative readmissions, as well as the increased likelihood of patients reporting more than 2 non‐medically managed attempts having higher BMIs at the time of evaluation, demonstrated the potential negative effect on presenting BMI of repeated dieting and weight cycling among patients presenting for surgery [36]. This suggests that preoperative weight cycling of repetitious over‐ or undereating may lead to poorer postoperative weight loss. It may also be that those patients engaged preoperatively in weight management in a healthcare system may have more medical risk, placing them at higher odds of readmission and complications.
It is worth noting that patients with higher BMIs who reported a history of non‐medically managed attempts do not have statistically different postoperative weight loss compared with those without higher preoperative BMIs and non‐medically managed attempts. This, combined with the lack of significant associations between any category of dieting attempts and %TWL, suggests that MBS may equalize patients' postsurgical weight loss. Future research should include %TWL data up to 24 months to examine if any differences would emerge at time points much further out from surgery.
Prior research on VLCDs for preoperative weight loss, while limited, has been mixed, with some finding that it reduced postoperative complications [37, 38] and others finding that it made no difference [39, 40]. The findings from this study may help to explain this discrepancy: reporting two or more previous attempts at VLCDs significantly increased the risk of postoperative complications, but having only one previous attempt was a protective factor instead. Future work could explore how preoperative weight loss mandates, which favor VLCDs for their high short‐term weight loss, contribute to postoperative complications when undergone repeatedly by a patient. Although there is no known literature covering low fat diets prior to MBS, future research should explore the role of low fat diets in postoperative readmissions and 30‐day complications.
There were few prominent demographic differences between patients utilizing different forms of dieting pre‐surgery, with the notable exception of non‐medically‐managed attempts, which were more likely to be reported by patients with public health insurance, who were fully or partially employed, had at least some college, and did not live alone. Although household income was not available in this study, the majority of these variables (employment, some college education, and cohabitation) aligned with higher economic status, in which fad diets, commercial diets, and over‐the‐counter supplements may be more available.
This study found no significant associations between ED diagnosis and postoperative outcomes. In prior work, it was established that patients who were diagnosed with ED during the pre‐operative psychological evaluation were delayed in proceeding to surgery [4, 5]. It is possible that these patients were provided with behavioral health referrals for ED treatment and acquired skills to manage disordered eating symptoms, which may have reduced their chances of having postoperative complications.
4.1. Limitations
This study had limitations that are important to consider when looking at future research. Perhaps most relevant is that these findings were based exclusively on self‐report data during patients' preoperative psychological evaluation, with no other standardized prompts or follow‐up questions employed by the psychologist to assess previous dieting attempts. It is possible that further assessment could have yielded more detailed, comprehensive, or accurate information about past dieting attempts. In addition, the categories used for analysis were created by the authors (with respect to the National Institutes of Health dietary guidelines) rather than using a validated scale. The psychological evaluations were completed by a single psychologist and the data only included the initial evaluation. While the demographics of the sample were in line with typical samples of bariatric surgery patients [41], they were not generalizable to male or gender nonconforming patients or patients from racial/ethnic minority groups. It is worth noting that patients may underreport problematic eating behaviors to avoid a delay for MBS. Indeed, socially desirable responses have been demonstrated in various samples of patients pursuing MBS [42]. Finally, this data was collected between 2019 and 2020, prior to the second generation of anti‐obesity medications such as semaglutide, setmelanotide, and tirzepatide [43]. Future studies should purposively include patients with MBS who have had experiences with these medications.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
LCS conceived the project, carried out the analyses, and wrote the majority of the paper. KJP assisted with analyses and writing. HMK collected the data and provided feedback on early drafts of the paper. AAP provided clinical expertise in contextualizing psychological variables and proofread the manuscript for person‐first language.
Funding: The authors received no specific funding for this work.
References
- 1. Buchwald H., Avidor Y., Braunwald E., et al., “Bariatric Surgery: A Systematic Review and Meta‐Analysis,” JAMA 292, no. 14 (October 13, 2004): 1724–1737, 10.1001/jama.292.14.1724. [DOI] [PubMed] [Google Scholar]
- 2. Dixon A. F., Dixon J. B., and O'Brien P. E., “Laparoscopic Adjustable Gastric Banding Induces Prolonged Satiety: A Randomized Blind Crossover Study,” Journal of Clinical Endocrinology and Metabolism 90, no. 2 (February 2005): 813–819, 10.1210/jc.2004-1546 [DOI] [PubMed] [Google Scholar]
- 3. Fischer S., Chen E., Katterman S., et al., “Emotional Eating in a Morbidly Obese Bariatric Surgery‐Seeking Population,” Obesity Surgery 17, no. 6 (June 2007): 778–784, 10.1007/s11695-007-9143-x. [DOI] [PubMed] [Google Scholar]
- 4. Kiser H. M., Pona A. A., Focht B. C., et al., “Associations Between Psychological Evaluation Outcomes, Psychiatric Diagnoses, and Outcomes Through 12 Months After Bariatric Surgery,” Surgery for Obesity and Related Diseases 19, no. 6 (June 2023): 594–603, 10.1016/j.soard.2022.12.018. [DOI] [PubMed] [Google Scholar]
- 5. Kiser H. M., Pratt K. J., Focht B. C., et al., “Preoperative Psychological Evaluation Outcomes, Reasoning, and Demographic and Diagnostic Correlates,” Obesity Surgery 33, no. 2 (February 2023): 539–547, 10.1007/s11695-022-06414-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Koball A. M., Clark M. M., Collazo‐Clavell M., et al., “The Relationship Among Food Addiction, Negative Mood, and Eating‐Disordered Behaviors in Patients Seeking to Have Bariatric Surgery,” Surgery for Obesity and Related Diseases 12, no. 1 (January 2016): 165–170, 10.1016/j.soard.2015.04.009. [DOI] [PubMed] [Google Scholar]
- 7. Dawes A. J., Maggard‐Gibbons M., Maher A. R., et al., “Mental Health Conditions Among Patients Seeking and Undergoing Bariatric Surgery: A Meta‐Analysis,” JAMA 315, no. 2 (January 2016): 150–163, 10.1001/jama.2015.18118. [DOI] [PubMed] [Google Scholar]
- 8. Münzberg H., Laque A., Yu S., Rezai‐Zadeh K., and Berthoud H.‐R., “Appetite and Body Weight Regulation After Bariatric Surgery,” Obesity Reviews 16, no. Suppl 1 (March 2016): 77–90, 10.1111/obr.12258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Amundsen T., Strømmen M., and Martins C., “Suboptimal Weight Loss and Weight Regain After Gastric Bypass Surgery—Postoperative Status of Energy Intake, Eating Behavior, Physical Activity, and Psychometrics,” Obesity Surgery 27, no. 5 (May 2017): 1316–1323, 10.1007/s11695-016-2475-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Sarwer D. B. and Heinberg L. J., “A Review of the Psychosocial Aspects of Clinically Severe Obesity and Bariatric Surgery,” American Psychologist 75, no. 2 (February‐March 2020): 252–264, 10.1037/amp0000550. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Shimizu H., Annaberdyev S., Motamarry I., Kroh M., Schauer P. R., and Brethauer S. A., “Revisional Bariatric Surgery for Unsuccessful Weight Loss and Complications,” Obesity Surgery 23, no. 11 (November 2013): 1766–1773, 10.1007/s11695-013-1012-1. [DOI] [PubMed] [Google Scholar]
- 12. Mitchell J. E., King W. C., Courcoulas A., et al., “Eating Behavior and Eating Disorders in Adults Before Bariatric Surgery,” International Journal of Eating Disorders 48, no. 2 (March 2015): 215–222, 10.1002/eat.22275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Marketdata Enterprises LLC . The U.S. Weight Loss and Diet Control Market. 17th ed. (Tampa, FL: Marketdata Enterprises LLC, March 2023). Report No.: 5313560. [Google Scholar]
- 14. Lowe M. R. and Timko C. A., “What a Difference a Diet Makes: Towards an Understanding of Differences Between Restrained Dieters and Restrained Nondieters,” Eating Behaviors 5, no. 3 (July 2004): 199–208, 10.1016/j.eatbeh.2004.01.006. [DOI] [PubMed] [Google Scholar]
- 15. Greenway F. L., “Physiological Adaptations to Weight Loss and Factors Favouring Weight Regain,” International Journal of Obesity 39, no. 8 (August 2015): 1188–1196, 10.1038/ijo.2015.59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Nordmo M., Danielsen Y. S., and Nordmo M., “The Challenge of Keeping it off, a Descriptive Systematic Review of High‐Quality, Follow‐Up Studies of Obesity Treatments,” Obesity Reviews 21, no. 1 (January 2020): e12949, 10.1111/obr.12949. [DOI] [PubMed] [Google Scholar]
- 17. Dayan P. H., Sforzo G., Boisseau N., Pereira‐Lancha L. O., and Lancha A. H. Jr, “A New Clinical Perspective: Treating Obesity With Nutritional Coaching Versus Energy‐Restricted Diets,” Nutrition 60 (April 2019): 147–151, 10.1016/j.nut.2018.09.027. [DOI] [PubMed] [Google Scholar]
- 18. Gilhooly C. H., Das S. K., Golden J. K., et al., “Food Cravings and Energy Regulation: The Characteristics of Craved Foods and Their Relationship With Eating Behaviors and Weight Change During 6 Months of Dietary Energy Restriction,” International Journal of Obesity 31, no. 12 (December 2007): 1849–1858, 10.1038/sj.ijo.0803672. [DOI] [PubMed] [Google Scholar]
- 19. Massey A. and Hill A. J., “Dieting and Food Craving. A Descriptive, Quasi‐Prospective Study,” Appetite 58, no. 3 (June 2012): 781–785, 10.1016/j.appet.2012.01.020. [DOI] [PubMed] [Google Scholar]
- 20. Schaumberg K. and Anderson D., “Dietary Restraint and Weight Loss as Risk Factors for Eating Pathology,” Eating Behaviors 23 (December 2016): 97–103, 10.1016/j.eatbeh.2016.08.009. [DOI] [PubMed] [Google Scholar]
- 21. van Strien T., “Causes of Emotional Eating and Matched Treatment of Obesity,” Current Diabetes Reports 18, no. 6 (April 252018): 35, 10.1007/s11892-018-1000-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Bettini S., Belligoli A., Fabris R., and Busetto L., “Diet Approach Before and After Bariatric Surgery,” Reviews in Endocrine & Metabolic Disorders 21, no. 3 (September 2020): 297–306, 10.1007/s11154-020-09571-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Bacon L. and Aphramor L., “Weight Science: Evaluating the Evidence for a Paradigm Shift,” Nutrition Journal 10, no. 69 (January 24, 2011): 9–22, 10.1186/1475-2891-10-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. McComb S. E. and Mills J. S., “Orthorexia Nervosa: A Review of Psychosocial Risk Factors,” Appetite 140 (September 1, 2019): 50–75, 10.1016/j.appet.2019.05.005. [DOI] [PubMed] [Google Scholar]
- 25. Gade H., Rosenvinge J. H., Hjelmesæth J., and Friborg O., “Psychological Correlates to Dysfunctional Eating Patterns Among Morbidly Obese Patients Accepted for Bariatric Surgery,” Obesity Facts 7, no. 2 (2014): 111–119, 10.1159/000362257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Hamilton A., Mitchison D., Basten C., et al., “Understanding Treatment Delay: Perceived Barriers Preventing Treatment‐Seeking for Eating Disorders,” Australian and New Zealand Journal of Psychiatry 56, no. 3 (March 2022): 248–259, 10.1177/00048674211020102. [DOI] [PubMed] [Google Scholar]
- 27. Deb S., Voller L., Palisch C., et al., “Influence of Weight Loss Attempts on Bariatric Surgery Outcomes,” American Surgeon 82, no. 10 (2016): 916–920, 10.1177/000313481608201012. [DOI] [PubMed] [Google Scholar]
- 28. Gibbons L. M., Sarwer D. B., Crerand C. E., et al., “Previous Weight Loss Experiences of Bariatric Surgery Candidates: How Much Have Patients Dieted Prior to Surgery?,” Surgery for Obesity and Related Diseases 2, no. 2 (2006): 159–164, 10.1016/j.soard.2006.03.013. [DOI] [PubMed] [Google Scholar]
- 29. Roehrig M., Maseb R. M., White M. A., Rothschild B. S., Burke‐Martindale C. H., and Grilo C. M., “Chronic Dieting Among Extremely Obese Bariatric Surgery Candidates,” Obesity Surgery 19, no. 8 (2009): 1116–1123, 10.1007/s11695-009-9865-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Chinaka U., Fultang J., Ali A., Rankin J., and Bakhshi A., “Pre‐specified Weight Loss Before Bariatric Surgery and Postoperative Outcomes,” Cureus 12, no. 12 (2020): e12406, 10.7759/cureus.12406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Gasoyan H., Tajeu G., Halpern M. T., and Sarwer D. B., “Reasons for Underutilization of Bariatric Surgery: The Role of Insurance Benefit Design,” Surgery for Obesity and Related Diseases 15, no. 1 (2019): 146–151, 10.1016/j.soard.2018.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Tewksbury C., Williams N. N., Dumon K. R., and Sarwer D. B., “Preoperative Medical Weight Management in Bariatric Surgery: A Review and Reconsideration,” Obesity Surgery 27, no. 1 (2016): 208–214, 10.1007/s11695-016-2422-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Mechanick J. I., Apovian C., Brethauer S., et al., “Clinical Practice Guidelines for the Perioperative Nutrition, Metabolic, and Nonsurgical Support of Patients Undergoing Bariatric Procedures—2019 Update: Cosponsored by 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,” Endocrine Practice 25, no. 12 (December 2019): 1346–1359. [DOI] [PubMed] [Google Scholar]
- 34. Brethauer S. A., Kim J., el Chaar M., et al., “Standardized Outcomes Reporting in Metabolic and Bariatric Surgery,” Surgery for Obesity and Related Diseases 11, no. 3 (May‐June 2015): 489–506, 10.1016/j.soard.2015.02.003. [DOI] [PubMed] [Google Scholar]
- 35. Nutrient Recommendations and Databases [Internet]. (National Institutes of Health Office of Dietary Supplements, 2016), https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx#dri. [Google Scholar]
- 36. Mackie G. M., Samocha‐Bonet D., and Tam C. S., “Does Weight Cycling Promote Obesity and Metabolic Risk Factors?,” Obesity Research & Clinical Practice 11, no. 2 (March‐April 2017): 131–139, 10.1016/j.orcp.2016.10.284. [DOI] [PubMed] [Google Scholar]
- 37. van Nieuwenhove Y., Dambrauskas Z., Campillo‐Soto A., et al., “Preoperative Very Low‐Calorie Diet and Operative Outcome After Laparoscopic Gastric Bypass: A Randomized Multicenter Study,” Archives of Surgery 146, no. 11 (November 2011): 1300–1305, 10.1001/archsurg.2011.273. [DOI] [PubMed] [Google Scholar]
- 38. Owers C. E., Abbas Y., Ackroyd R., Barron N., and Khan M., “Perioperative Optimization of Patients Undergoing Bariatric Surgery,” Journal of Obesity 2012 (2012): 781546, 10.1155/2012/781546. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Holderbaum M., Casagrande D. S., Sussenbach S., and Buss C., “Effects of Very Low Calorie Diets on Liver Size and Weight Loss in the Preoperative Period of Bariatric Surgery: A Systematic Review,” Surgery for Obesity and Related Diseases 14, no. 2 (February 2018): 237–244, 10.1016/j.soard.2017.09.531. [DOI] [PubMed] [Google Scholar]
- 40. Gils Contreras A., Bonada Sanjaume A., Montero Jaime M., et al., “Effects of Two Preoperatory Weight Loss Diets on Hepatic Volume, Metabolic Parameters, and Surgical Complications in Morbid Obese Bariatric Surgery Candidates: A Randomized Clinical Trial,” Obesity Surgery 28, no. 12 (December 2018): 3756–3768, 10.1007/s11695-018-3413-7. [DOI] [PubMed] [Google Scholar]
- 41. Welbourn R., Hollyman M., Kinsman R., et al., “Bariatric Surgery Worldwide: Baseline Demographic Description and One‐Year Outcomes From the Fourth IFSO Global Registry Report 2018,” Obesity Surgery 29, no. 3 (March 2019): 782–795, 10.1007/s11695-018-3593-1. [DOI] [PubMed] [Google Scholar]
- 42. Butt M., Wagner A., and Rigby A., “Associations of Social Desirability on Psychological Assessment Outcomes for Surgical Weight Loss Patients,” Journal of Clinical Psychology in Medical Settings 28, no. 2 (2021): 384–393, 10.1007/s10880-020-09725-5. [DOI] [PubMed] [Google Scholar]
- 43. Wadden T. A., Chao A. M., Moore M., et al., “The Role of Lifestyle Modification With Second‐Generation Anti‐Obesity Medications: Comparisons, Questions, and Clinical Opportunities,” Current Obesity Reports 12, no. 4 (2023): 453–473, 10.1007/s13679-023-00534-z. [DOI] [PMC free article] [PubMed] [Google Scholar]