INTERNATIONAL JOURNAL OF CLINICAL AND MEDICAL CASES (ISSN:2517-7346)

Previous Issue  |   Current Issue  |   Archive  |   Recent Articles  |   Publications      
     PDF Download

Predicting Outcomes Following Laparoscopic Adjustable Gastric Banding Surgery: Weight Loss versus Improved Perceptions of Appearance

Jude Hancock1*, Sue Jackson2, Andrew Johnson1

1 Department of Diabetes and Endocrinology, North Bristol NHS Trust, Bristol, United Kingdom
2 Centre for Health and Clinical Research,  University of the West of England, United Kingdom

CitationCitation COPIED

Hancock J, Jackson S, Johnson A. Predicting Outcomes Following Laparoscopic Adjustable Gastric Banding Surgery: Weight Loss versus Improved Perceptions of Appearance. Int J Clin Med Cases. 2018 Apr;1(2):108

Copyright:

 © 2018 Hancock J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 international License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: The number of overweight individuals continues to rise; some of these individuals require bariatric surgery to assist weight loss. In order to determine which individuals are most likely to benefit from surgery there is a need to know which factors predict success. To date, the focus of success has been on weight loss, but improvements in psychological factors are also important.

Method: A longitudinal study over five years with 73 participants having laparoscopic adjustable gastric banding [LAGB] surgery (58 female, 50 with diabetes, aged between 30 to 74 years (mean ± standard deviation, 46.3 ± 8.9). Scores from the Hospital Anxiety and Depression Scale [HADS], and Derriford Appearance Scale [DAS-24] were recorded along with participants’ weight. Two separate linear regression analyses were performed to predict the 5 year outcomes for; a) DAS-24 scores, and b) Percentage Excess Body Weight Lost [%EBWL].

Results: Rates of social anxiety (DAS-24) were predicted by three variables; age group, pre-LAGB HADS anxiety and depression categories. %EBWL could not be predicted from the dependent variables (gender, age group, pre-LAGB BMI, pre-LAGB diabetic status, DAS24 score at baseline, and the pre-LAGB HADS anxiety and depression categories).

Conclusion: Although the variables used in this study could not predict %EBWL following LAGB, rates of social anxiety associated with appearance (DAS-24) could be predicted by three variables. The DAS-24 findings highlight the importance of assessing an individual’s mental health status prior to LAGB surgery.

Keywords

Laparoscopic Adjustable Gastric Banding; Outcomes; Longitudinal; Obesity; Prediction

Article Highlights

  • Age group (aged ≤ 49 years or aged ≥ 50 years) is a significant predictor of emotional and behavioural problems (social anxiety and social avoidance related to appearance) as measured by the Derriford Appearance Scale [DAS-24] at five years post laparoscopic adjustable gastric banding surgery.
  • Measures of general anxiety and depression in the form of the Hospital Anxiety and Depression Scale strengthen the prediction of the long term scores on the DAS-24.
  • Assessing and addressing mental health problems prior to surgery would be beneficial in ensuring maximum long-term psychosocial benefits.

Introduction

A report published in 2017 using data collected between 1990 and 2015 stated that 603.7 million adults worldwide are considered obese [1]. To be classified as obese, individuals have to have a body mass index (BMI) ≥ 30 kg/m [2]. A large body of literature recognises that tackling obesity not just about reducing weight; outcomes such as improving mental health and self-perceptions are also important [1,3-6]. The preferred treatment for overcoming obesity is behavioural modification of an individual’s maladaptive diet and exercise habits7-10, however, for some individuals bariatric surgery is an additional necessity to change food-related behaviours [2,8,11].

For many years the focus on outcome following bariatric surgery was weight loss, specifically individuals achieving > 50% excess body weight loss (EBWL) in the first two to three years following surgery [12]. EBWL < 20% was viewed as a failed surgical intervention [13]. However, evidence demonstrates that reducing EBWL by 5-10% can lead to significant reductions in the co-morbidities known to be associated with obesity such as cardiovascular disorders and diabetes, and for individuals who are obese these positive health benefits are unlikely to be achieved without surgical intervention [7,14,15]. More recently other positive outcomes in addition to weight loss have been considered as successes of bariatric surgery, such as improvement in quality of life, and improvements in mental health [3,5].

The ability to work out the factors that predict an outcome is a central feature of health research as it enables health care professionals to determine which individuals will benefit most from an intervention [16-19]. This is important for individuals undergoing bariatric surgery, as although this intervention is beneficial, it is expensive [15,20]. Factors shown to predict weight loss include; being aged < 40 years, and BMI < 50 kg/m2 prior to surgery, depression levels prior to surgery, and adherence to post-surgical eating guidelines [13,19,21]. Despite this knowledge there is still a requirement to further explore predictors of long-term success22, particularly in relation to issues of appearance related to obesity.

Overweight individuals are often stigmatised in society due to their appearance [23,24]. This stigmatisation often contributes to continued maladaptive behaviour, such as comfort eating, which can negatively impact on weight loss and maintenance [25,26]. Being visibly different creates challenges in activities such as using public seating (including within the workplace) and travelling by air, train or bus, as assistance is often required to adapt the environment [27- 29]. Measurement of appearance difficulties and the impact on an individual is possible. One scale which explores the emotional and behavioural difficulties people experience due to appearance is the Derriford Appearance Scale [DAS-24] [30]. This scale is a shortened version of the DAS-59 [31]. Both scales have undergone rigorous psychometric testing during development. The advantage of the DAS24 is the length of the scale when used as part of a battery of tests. In our own research we have used the DAS-24 to explore changes in perceptions of appearance following laparoscopic adjustable gastric banding [LAGB] surgery, both short-term (six months to one year following surgery) and longer-term (five years following surgery) [32,33]. Our findings indicate improvements are present following surgery, which are sustained long-term. However, to date, there are no published studies exploring what factors contribute to using this scale as a predictor of outcome. Therefore, the aims of the current research were two-fold. First, to explore predictors of emotional and behavioural difficulties due to problems with appearance using the DAS-24 scale, and second to explore predictors of %EBWL five years post-LAGB surgery in the current sample.

Method

Design
A prospective five year, single-centre longitudinal study.

Participants
Participants were recruited between 1st January 2007 and 31st December 2009 from a Weight Loss Service [WLS] in a National Health Service [NHS] hospital in the South West of England, United Kingdom [UK]. Seventy-three participants were recruited, of these 58 (79.5%) were female, and 50 (68.5%) were living with diabetes. Age at time of surgery ranged from 30 to 74 years (mean ± standard deviation [SD], 46.3 ± 8.9). One participant reported their ethnicity as Indian, while all others identified themselves as White. In order to receive LAGB surgery participants needed to meet the National Institute for Health and Care Excellence eligibility criteria [11].

Measures
At baseline participants were asked to record their demographic information (gender, age, and ethnicity). In addition the participants’ diabetic status (living with diabetes or not) was recorded. At every visit, participants were weighed on calibrated scales, and completed two standardised measures. The DAS-24 consists of 24 items, with response options on a 4-point Likert scale ranging from 1 to 4. Response items vary between questions (e.g., “I avoid communal changing rooms” anchored from “almost always” to “never/almost never”; and “How rejected do you feel?” anchored from “not at all” to “extremely”), with 11 items having a “not applicable” [N/A] option scored as 0. Higher scores on the scale indicate higher levels of social anxiety associated with social avoidance as a result of appearance concerns. There are no suggested clinical cut-offs for this questionnaire, but normative comparison values are given as 26.63 ± 11.40 (mean ± SD). The Hospital Anxiety and Depression Scale [HADS] comprises two subscales (seven items in each) measuring general anxiety and depression [34]. For this scale, respondents are asked to answer questions based on how they have been feeling in the past week, rated on a 4-point Likert scale ranging from 0 to 3. Response anchors vary between questions (e.g., “I can sit at ease and feel relaxed” anchored from “definitely” to “not at all”; and “I get sudden feelings of panic” anchored from “not at all” to “very much indeed”). Higher scores on the HADS indicate greater distress. The questionnaire authors suggest that scores are grouped to act as signifiers of distress. In its current form, the HADS is divided into four ranges: normal (0-7), mild (8-10), moderate (11-15), and severe (16-21).

Procedure
Ethical approval was obtained from North Bristol NHS Trust Research Ethics Committee [REC] (REC Ref: 06/Q2002/38). Written informed consent was obtained from all participants prior to study participation. Each data collection point typically coincided with the participant’s routine visit at the WLS where they completed the study measures. If a participant did not attend, questionnaires were posted with a pre-paid envelope to enable return to the WLS.

Data Analysis

Analyses were undertaken using SPSS version [23]. Descriptive statistics (mean and SD) were calculated for every measure at each data collection point. Body Mass Index [BMI] and %EBWL was calculated for each participant at every visit using recommended methods [35,36]. Participants were assigned to dichotomous groups for age (aged ≤ 49 years or aged ≥ 50 years, n = 46 & 27 respectively) and BMI (≤ 49 kg/m2 or ≥ 50 kg/m2, n = 28 & 45 respectively), which are commonly used groupings in LAGB research [13,21,37,40,41]. In addition, participants were assigned to dichotomous categories for pre-LAGB levels of depression and anxiety (e.g., not depressed if the score was ≤ 7, or depressed if the score was ≥ 8 with the same cutoffs used for anxiety). As is typical in longitudinal studies, missing data was present, in these instances the last observation carried forward [LOCF] method was applied [42-44].

Linear regression analysis was performed. In the first regression the DAS-24 scores at five years post-LAGB was entered as the dependent variable, with gender, age group, pre-LAGB BMI and preLAGB diabetic status as the independent predictor variables on step one, with the pre-LAGB HADS anxiety and depression categories on step two [45,46]. In the second regression the %EBWL value at five years post-LAGB was entered as the dependent variable, with gender, age group, pre-LAGB BMI and pre-LAGB diabetic status as the independent predictor variables on step one, and the DAS-24 score at baseline, and the pre-LAGB HADS anxiety and depression categories on step two. Recommended techniques to test for multicollinearity were used [45].

Results

Missing Data
Table 1 shows the number of LOCF measures used at each time point for the DAS-24 and %EBWL measures. The information presented clearly shows that as time since surgery increases so does the amount of missing data. There is a larger percentage of missing DAS-24 data than weight data at each time point, suggesting participants found it more acceptable to be weighed than to fill in a questionnaire. 

Regression
There were no issues with multicollinearity in either of the regressions as the Variance Inflation Factor [VIF] scores were below 10, and the average VIF score was not substantially greater than 1. Furthermore the tolerance scores were around 1 [45]. Table 2 shows the preoperative and five years post-LAGB scores for each of the measures. In addition, prior to surgery, 46 (63.0%) participants had signs of anxiety, and 44 (60.3%) had signs of depression (i.e. scoring more than 7 on the HADS subscales). A chi-squared analysis showed there were more participants experiencing both anxiety and depression prior to LAGB than individuals who were psychologically healthy; (49.3% versus 26.0% respectively), χ²(1) = 16.8, p < 0.001.

The results of the first regression with the DAS-24 as the dependent variable resulted in one predictor explaining 17.5% of the variance in step one (R2 = 0.17, F(4,68) = 3.61, p = 0.01). It was found that age group significantly predicted DAS-24 at five years post-LAGB (β = -0.26, p = 0.03). However, in step two, three predictors explained 49.2% of the variance (R2 = 0.49, F(6,66) = 10.7, p < 0.001). Age remained a significant predictor of social anxiety (DAS-24) at five years post-LAGB (β = -0.21, p = 0.03), with general anxiety (β = 0.23, p = 0.03), and depression strengthening the prediction (β = 0.43, p < 0.001). Using the constant and unstandardized β-values to calculate the relationship between the predictors and DAS-24 scores, results showed the predicted DAS-24 scores for participants aged ≤ 49 years are 8.1 points higher (i.e., they have more emotional and behavioural problems due to appearance) than participants aged ≥ 50 years five years post-LAGB. Similarly, participants who were experiencing symptoms of general anxiety (HADS) pre-LAGB have predicted DAS24 scores 9.0 points higher five years post-LAGB than those who were healthy in this regard, while participants who were experiencing symptoms of depression (HADS) pre-LAGB have predicted DAS-24 scores 16.4 points higher five years post-LAGB than those whose scores on this HADS subscale were < 7.

In the second regression, with %EBWL as the dependent variable, in neither step one or two did any of the entered predictors explained the variance, R2 = 0.02, F(4,68) = 1.3, p = 0.28, and R2 = 0.00, F(7,65) = 1.0, p = 0.44, respectively.


Table 1: Number (%) of LOCF used at each time point post-LAGB surgery


Table 2: Descriptive statistics (mean ± SD) of the measures preoperatively and five years post-LAGB

Discussion

This exploration of the predictors of long-term appearance related social anxiety (DAS-24) and %EBWL had mixed findings. The DAS-24 results showed that preoperative age group, and general anxiety and depression were all significant. Specifically, the presence of symptoms of general anxiety and depression prior to LAGB had a significant negative impact on the reduction of emotional and behavioural problems due to appearance five years following surgery. Additionally, individuals aged ≤ 49 years were more likely to still be experiencing social anxiety and social avoidance in the long term than individuals aged ≥ 50 years, a finding reported during the development of the DAS-24 scale [30].

In terms of %EBWL, it appears that in the current sample the predictors used do not significantly explain the variance. In the five years post-LAGB this study was not able to predict the factors that are likely to be attributed to success, and we can see this sample on average did not achieve the 50% EBWL figure typically cited as expected for individuals post-LAGB [47]. However, five years post LAGB the sample did have a sustained > 25% EBWL which should have positive long-term health benefits [47,48]. As far as the authors are aware, this the first time an exploration of the predictors of a quantitative improvement in psychosocial functioning as it relates to the appearance aspects of obesity (as measured by the DAS-24) in a sample of individuals undergoing LAGB surgery has been undertaken.

Of note in this study is that gender was not a predictor of either DAS-24 or %EBWL. Females typically experience more emotional and behavioural problems due to appearance than males, along with depression as a result of obesity [45,49-51]. The absence of gender as a predictor may be due to the small unequal gender sample sizes, or the psychological predictors used in this study. For example, using a different psychological measure as the independent variable, such as personality, may have revealed gender as a predictor which has been demonstrated in research in a UK sample where results showed distinctly different relationships between men and women with regards to body weight and personality [52]. Furthermore, it is possible that the DAS-24 may not be sensitive enough to show known gender differences with regards to problems due to appearance in obese individuals. As far as the authors are aware this is the first time the DAS-24 has been used in this way, and further research would be required in a larger sample to validate these findings.

Findings from this study confirms previous research which indicate that the presence of anxiety and/or depression prior to LAGB surgery appears to have a lasting negative impact on mental health, as measured by the DAS-24 in the current study [22]. Furthermore, results indicate that an individual’s mental health needs to be assessed, and if appropriate treated prior to and throughout their weight loss journey in order to maximise mental and physical health benefits from the surgical intervention [11,53,54]. Previous analysis on the current data set exploring changing in mental health over the five year period following surgery has indicated improvements from pre-surgical measurements [33,37,38].

It is an established fact that diabetes is a common co-morbidity in obese individuals [2,7,11]. Adherence to diabetes medication can influence weight control, which in turn affects appearance [39]. Over half the individual’s in the current study were living with diabetes, yet this was not a predictor for either %EBWL or DAS-24 outcomes. Issues with appearance as measured by the DAS-24 are arguably unlikely to be related to diabetic status in adults, but in an adolescent sample this may be a predictive factor which would be worth exploring in future research.

Strengths and Limitations

In terms of the gender split within the current sample, this was typical of candidates for LAGB surgery with 80% generally being female, however, the skew towards a predominately female sample makes the findings not necessarily generalizable to populations undergoing LAGB where there are more equal splits [40]. This is similar to the lack of ethnic minorities in the current sample, a point we have discussed elsewhere [33,37].

The sample size was small, however, guidelines suggest that a sample where n > 60 should be sufficient to identify predictors [45,55]. We can therefore cautiously assume that the factors that were significant predictors of the DAS-24 scores are likely to remain  in larger samples. The small sample may have been a contributing factor to the inability to find predictors of % EBWL. Another factor may have been the use of the LOCF method. The carried forward changes in DAS-24 may have been greater than the carried forward changes in %EBWL between time points, re-running the data with only those participants with a five year post-LAGB %EBWL recorded would not be reasonable as the sample would be n = 46 and therefore too small for regression analysis.

The missing data, although typical in longitudinal research, also may be indicative of another issue with participants in weight loss studies. At six months, missing data was present for the DAS-24 scale but not %EBWL suggesting participants may have attended the appointment but chose not to complete the questionnaire. As time since surgery increased, the number of missing DAS-24 questionnaires continued to be higher than %EBWL data. This may indicate that following LAGB participants are more concerned about their changes in weight rather than their changes in psychological health. These individuals are attending a WLS where the primary focus of their visits is on the amount of weight they are losing, however, it is well documented that psychological health is important for weight loss and maintenance [3,13,18,19,56]. It is possible that at routine post-surgery follow-up appointments the patient-clinician discussions are concentrating on behavioural changes required to lose weight, and psychological adaption following surgery may be overlooked.

Conclusions

The presence of anxiety and/or depression prior to LAGB surgery and the age group an individual belongs to are all predictive factors for the long-term improvements of social anxiety associated with appearance. However, %EBWL appears not to be predictable from the dependent measures used in the current study. These findings highlight the importance of assessing the mental health of individuals undergoing LAGB prior to surgery in order to support change if required to ensure maximum long-term benefits from surgical intervention.

Funding

This work was supported by money generated through commercial research studies and a charitable fund.

Disclosure Statement

The authors declare no conflict of interest.

References

  1. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al.Health effects of overweight and obesity in 195 countries over 25years. N Eng J Med. 2017 Jul;377(1):13-27.
  2. Colquitt JL, Pickett K, Loveman E, Frampton GK. Surgeryfor weight loss in adults. Cochrane Database Syst Rev. 2014Aug;(8):CD003641.
  3. Bocchieri LE, Meana M, Fisher BL. A review of psychosocialoutcomes of surgery for morbid obesity. J Psychosom Res. 2002Mar;52(3):155-165.
  4. Chang VW, Christakis NA. Self-perception of weightappropriateness in the United States. Am J Prev Med. 2003May;24(4):332-339.
  5. Magallares A, Schomerus G. Mental and physical health-relatedquality of life in obese patients before and after bariatric surgery:a meta-analysis. Psychol, Health Med. 2015;20(2):165-176.
  6. Tylka TL, Annunziato RA, Burgard D, Daníelsdóttir S, Shuman E,et al. The weight-inclusive versus weight-normative approach tohealth: Evaluating the evidence for prioritizing well-being overweight loss. J Obesity. 2014 Jul;2014:18.
  7. Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, et al.Foresight. Tackling obesities: future choices – modelling futuretrends in obesity & their impact on health (2nd Ed.). 2007.
  8. Kendrick ML, Dakin GF. Surgical approaches to obesity. Mayo ClinProc. 2006 Oct;81(10):S18-S24.
  9. Johnson BT, Scott-Sheldon LA, Carey MP. Meta-synthesis ofhealth behavior change meta-analyses. Am J Public Health. 2010Nov;100(11):2193-2198.
  10. NICE. Managing overweight and obesity in adults – lifestyleweight management services. 2014.
  11. NICE. Obesity: Identification, assessment and management ofoverweight and obesity in children, young people and adults.2014.
  12. Buchwald H, Avidor Y, Braunwald E, Jensen MD, Pories W, et al.Bariatric surgery: a systematic review and meta-analysis. JAMA.2004 Oct ;292(14):1724-1737.
  13. Busetto L, Segato G, De Marchi F, Foletto M, De Luca M, et al.Outcome predictors in morbidly obese recipients of an adjustablegastric band. Obes Surg. 2002 Feb;12(1):83-92.
  14. Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, et al. Look AHEAD Research Group. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care. 2011Jul;34(7):1481-1486.
  15. Gulliford MC, Charlton J, Prevost T, Booth H, Fildes A, et al. Costs and Outcomes of Increasing Access to Bariatric Surgery: Cohort Study and Cost-Effectiveness Analysis Using Electronic Health Records. Value Health. 2017 Jan;20(1):85-92.
  16. Conner M. Norman P. (Eds.) Predicting Health Behaviour. 2nd ed. Maidenhead, England: Open University Press. 2005.
  17. Finlay KA, Trafimow D, Moroi E. The importance of subjective norms on intentions to perform health behaviors. J App Soc Psychol. 1999 Jul;29(11):2381-2393.
  18. Herpertz S, Kielmann R, Wolf AM, Hebebr and J, Senf W. Do psychosocial variables predict weight loss or mental health after obesity surgery? A systematic review. Obes Res. 2004Oct;12(10):1554-69.
  19. Robinson AH, Adler S, Stevens HB, Darcy AM1, Morton JM, etal. What variables are associated with successful weight loss outcomes for bariatric surgery after 1 year? Surg Obes Relat Dis.2014 Jul-Aug;10(4):697-704.
  20. Picot J, Jones J, Colquitt JL, Gospodarevskaya E, Loveman E, et al. The clinical effectiveness and cost-effectiveness of bariatric(weight loss) surgery for obesity: a systematic review andeconomic evaluation. Health Technol Assess. 2009 Sep;13(41):1-190.
  21. Averbukh Y, Heshka S, El-Shoreya H, Flancbaum L, Geliebter A, et al. Depression score predicts weight loss following Roux-en-Y gastric bypass. Obes Surg. 2003 Dec;13(6):833-836.
  22. Wimmelmann CL, Dela F, Mortensen EL. Psychological predictors of mental health and health-related quality of life after bariatric surgery: a review of the recent research. Obes Res Clin Pract.2014 Jul-Aug;8(4):e314-324.
  23. Friedman KE, Reichmann SK, Costanzo PR, Zelli A, Ashmore JA, et al. Weight stigmatization and ideological beliefs: relation to psychological functioning in obese adults. Obes Res. 2005May;13(5):907-916.
  24. O’Brien KS, Latner JD, Ebneter D, Hunter JA. Obesity discrimination: the role of physical appearance, personal ideology, and anti-fat prejudice. Int J Obes (Lond). 2013 Mar;37(3):455-460.
  25. Sutin A, Robinson E, Daly M, Terracciano A. Weight discrimination and unhealthy eating-related behaviors. Appetite. 2016Jul;102:83-89.
  26. Puhl RM, Quinn DM, Weisz BM, Suh YJ. The role of stigma in weight loss maintenance among US adults. Ann Behav Med. 2017Oct;51(5):754-763.
  27. Buckle P, Buckle J. Obesity, ergonomics and public health. Perspect Public Health. 2011 Jul;131(4):170-176.
  28. Puhl R, Brownell KD. Bias, discrimination, and obesity. Obes Res.  2001 Dec;9(12):788-805.
  29. Small J, Harris C. Obesity and tourism: Rights and responsibilities. Ann Tourism Res. 2012 Apr;39(2):686-707.
  30. Carr T, Moss T, Harris D. The DAS24: a short form of the Derriford Appearance Scale DAS59 to measure individual responses to living with problems of appearance. Br J Health Psychol. 2005May;10(Pt 2):285-298.
  31. Carr T, Harris D, James C. The Derriford Appearance Scale(DAS‐59): A new scale to measure individual responses to living with problems of appearance. Brit J Health Psych. 2000May;5(2):201-215.
  32. Jackson S, Morris M, Lilley KT, Johnson AB. Laparoscopic Gastric Banding Surgery (LAGB) for Patients with Type 2 Diabetes and Morbid Obesity: Improving BMI and Psychological Status at 1Year Post-Surgery. Proceeding of the Endocrine Society’s 92ndAnnual Meeting, 2010; Jun 19–22, San Diego, USA.
  33. Hancock J, Jackson S, Johnson AB. The Long-term Psychological Impact of Disclosing (or not) Laparoscopic Adjustable Gastric Banding Surgery. Bariatr Surg Pract P. 2017.
  34. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983 Jun;67(6):361-370.
  35. Busetto L, Mozzi E, Schettino AM, Furbetta F, Giardiello C, et al. Three years durability of the improvements in health-related quality of life observed after gastric banding. Surg Obes Relat Dis.2015 Jan-Feb;11(1):110-117.
  36. Jenkins JR. Percentage of excess body weight lost (%EBWL). Seirra Bariatric Surgery. 2006;3:1
  37. Hancock J, Jackson S, Johnson AB. Under and over 50: exploring long-term weight-loss outcomes following laparoscopic adjustable gastric band by age and body mass index group. Surg Obes Relat Dis. 2016 Sep - Oct;12(8):1616-1621.
  38. Hancock J, Jackson S, Johnson AB. Take your pick - weight loss or clothes size goal: A longitudinal exploration of outcomes in individuals following laparoscopic adjustable gastric banding. Surg Rehab. 2017;1:1-7.
  39. Murphy LB, Thompson RJ, Morris MA. Adherence behavior among adolescents with types I insulin-dependent diabetes mellitus: the role of cognitive appraisal processes. J Pediatr Psychol. 1997;22(6):811-825.
  40. Lewis M, Netz U, Mizrahi S, Avinoah E, Gal D, et al. Do Male Patients Benefit from Laparoscopic Adjustable Gastric Banding More than Female Patients? A Retrospective Cohort Study. Obes Surg. 2018Mar;28(3):760-766.
  41. Tammaro P, Hansel B, Police A, Kousouri M, Magnan C, et al. Laparoscopic Adjustable Gastric Banding: Predictive Factors for Weight Loss and Band Removal After More than 10 Years’ Follow Up in a Single University Unit. World J Surg. 2017 Aug;41(8):2078-2086.
  42. Donald H. Robert GD. Missing data in longitudinal studies. In H. Donald & GD. Robert (Eds.). Longitudinal data analysis. Hoboken, New Jersey, USA: John Wiley & Sons; 2006:279-312.
  43. Siddique J, Brown CH, Hedeker D, Duan N, Gibbons RD, et al. Missing data in longitudinal trials–part B, analytic issues. Psychiat Ann. 2008 Dec;38(12):793-801.
  44. Shao J. Zhong B. Last observation carry‐forward and last observation analysis. Stat Med. 2003 Aug;22(15):2429-41.
  45. Field A. Discovering statistics using SPSS. London: Sage publications. 2009.
  46. Munro BH. Page EB. Statistical methods for health care research.2nd ed. Philadelphia, USA: JB. Lippincott Company. 1993.
  47. Mathus-Vliegen EM, de Weerd S, de Wit LT. Health-related quality of-life in patients with morbid obesity after gastric banding for surgically induced weight loss. Surgery. 2004 May;135(5):489-497.
  48. Barte JCM, Ter Bogt NCW, Bogers RP, Teixeira PJ, Blissmer B, etal. Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review. Obes Rev. 2010Dec;11(12):899-906.
  49. Grogan S. Body image: Understanding body dissatisfaction in men, women and children. 3rd ed. London, UK: Taylor & Francis.2016.
  50. Pliner P, Chaiken S, Flett GL. Gender differences in concern with body weight and physical appearance over the life span. Pers Soc Psychol B. 1990 Jun;16(2):263-273.
  51. Li L, Gower BA, Shelton RC, Wu X. Gender-specific relationship between Obesity and Major Depression. Front Endocrinol.2017;8:292.
  52. Faith MS, Flint J, Fairburn CG, Goodwin GM, Allison DB. Gender differences in the relationship between personality dimension sand relative body weight. Obes Res. 2001 Oct;9(10):647-650.
  53. LeMont D, Moorehead MK, Parish MS, Reto CS, Ritz SJ. Suggestionsfor the pre-surgical psychological assessment of bariatric surgery candidates. Am S Bariat Surg. 2004;129.
  54. McMahon MM, Sarr MG, Clark MM, Gall MM, Knoetgen J, etal. Clinical management after bariatric surgery: value of a multi disciplinary approach. Mayo Clin Proc. 2006 Oct;81(10Suppl):S34-45.
  55. Green SB. How many subjects does it take to do a regression analysis. Multivariate Behav Res. 1991 Jul;26(3):499-510.
  56. Elfhag K, Rössner S. Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain. Obes Rev. 2005 Feb;6(1):67-85.

Copyright © 2020 Boffin Access Limited.