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admin | frugal living tips and ideas | 03.02.2016
Binge eating is a pattern of disordered eating which consists of episodes of uncontrollable eating. While people tend to over eat from time to time, a consistent habit of frequent consumption of large amounts of food in a short period of time usually leads to weight gain and obesity. Most people with binge eating disorder have tried to control it on their own, but have not been able to control it for very long. If you suspect you might be suffering from an eating disorder , gain some knowledge by doing the Eating disorder Quiz. The most problematic health consequences of this type of eating disorder are brought on by the weight gain resulting from binging episodes.
Those who are obese and also have binge eating disorder are at risk for type 2 diabetes, high blood pressure, high blood cholesterol levels, gallbladder disease, heart disease, and certain types of cancer.
Obese people with binge eating disorder often feel bad about themselves and may avoid social gatherings. Most people who have eating binges try to hide this behavior from others, and often feel ashamed about being overweight or depressed about their overeating.
Those who binge eat, whether obese or not, feel ashamed, are well aware of their disordered eating patterns, and try to hide their problems. Although people who do not have any eating disorder may occasionally experience episodes of overeating, frequent binge eating is often a symptom of an eating disorder.
Often they become so good at hiding it that even close friends and family members are unaware that they binge eat.
The LASSO analysis revealed that higher scores on the Shape Concerns subscale of the EDE-Q, a higher frequency of binge eating episodes and vomiting, as well as higher depression scores significantly increased the probability of dropout. These interventions provide easy access to help for individuals who would otherwise delay seeking treatment and, hence, risk developing a more chronic course [2,3].
The advantages of Web-based interventions seem to be especially valuable in individuals with eating disorders, who are known to seek face-to-face treatment relatively late [4] because of feelings of shame and fear of stigma [5]. Therefore, Web-based interventions for individuals with eating disorders [6-10], their caregivers [11], and individuals at risk for eating disorders [12] have been developed.
Our analysis identified 4 indexes of symptom severity that predicted dropout, namely the EDE-Q Shape Concerns subscale, frequency of vomiting within the previous 28 days, frequency of binge eating within the previous 28 days, and the HSCL-25 depression subscale.
Neither motivation to change nor age predicted dropout.The overall level of symptom severity in the present sample was high when comparing the scores of the present sample with the published norms of the EDE-Q [51]. Whereas most of these studies investigated Web-based programs for women with bulimic symptoms, online interventions for patients with anorexia nervosa are rare [15].However, the limitation of such accessible interventions is that they allow participants to easily terminate (ie, with a mouse click) online treatment prematurely [16]. This shows that a large proportion of participants suffered from significant eating disorder pathology, although diagnoses were not made by means of structured clinical interviews. Accordingly, dropout rates for online interventions can be high, ranging from 3% to 81% across trials [17].
Therefore, the sample seems to be suitable for drawing assumptions about women with eating disorders in general.Similarly, the overall level of the dropout rate is comparable to that from other Web-based programs in general [17]. The high variance in dropout rates seems to be at least partially because of differences in the intensity and nature of the personal contact offered [18-20]. The current program was completely anonymous, which might have made it easier for participants to prematurely terminate their participation.
For example, patients who receive telephone contact instead of only email contact with the therapist have a higher likelihood of staying in online treatment [21].Beyond the generally elevated dropout rates of Internet interventions, disorder-specific differences also need to be considered. Future research should evaluate the importance of anonymity in Web-based interventions for individuals with eating disorders and determine its role in dropout.The dropout rate was equally distributed across the 6 sessions. Specifically, individuals with eating disorders may be at particular risk of dropping out from online treatments given their elevated dropout rates from face-to-face treatments [22,23]. However, it remains unclear whether the reasons for early versus late dropout are identical [22,39].
Dropout from face-to-face interventions for eating disorders generally ranges from 20% to 40% [22,23].
It is possible that early dropout is primarily because of participants’ dissatisfaction with the program, whereas late dropout may also be because of progress withdrawal (ie, participants feeling that they have already benefited enough) [22,39,68]. Research conducted in these settings has identified a number of predictors of treatment dropout for eating disorders.
Speculative reasons for program withdrawal in the study may have included feeling dissatisfied with the motivational content and the absence of practical advice and strategies for overcoming the eating disorder, feeling overwhelmed by negative emotions when dealing with one’s own reasons for and against the eating disorder, or having resolved motivational issues faster than in 6 sessions [22].
Because dropout was distributed evenly across the 6 sessions, the specific content of any particular session can be discarded as a cause of dropout.Although the high dropout rate is fairly typical for Web-based interventions [19], a high dropout rate is related to poorer outcome [16], meaning that it is important to understand the factors predicting early termination. Concerning the predictors of dropout, various indexes of symptom severity (ie, the EDE-Q Shape Concerns subscale as well as the frequency of binge eating and vomiting) predicted premature discontinuation of the Internet program.

Although the majority of studies have found a higher risk of dropout for patients with higher depression scores [29,30], this effect did not reach significance in a meta-analysis [23]. The finding regarding Shape Concerns is consistent with results from research in women with eating disorders in face-to-face settings [25,26] as well as previous Web-based interventions [6], which also showed that higher body dissatisfaction makes treatment dropout more likely. Additionally, age has been identified as a predictor of staying in treatment in bulimia nervosa because older patients tend to persist longer with face-to-face treatment than younger patients [23].
The finding that the higher the frequency of binge eating and vomiting, the more likely participants were to terminate treatment early is also in-line with previous findings.
However, 2 recent meta-analyses found that psychopathology, symptom severity, and age are not stable predictors of dropout from face-to-face therapies for women with eating disorders [22,23].Another potential predictor of treatment dropout is motivation to change (for a review see [28]) because it has been shown that participants with initially low levels of motivation to change their eating disorder symptoms [31,32] or low cooperativeness scores [27] are more likely to drop out of treatment. Therefore, interventions to enhance motivation to change in women with eating disorders have been implemented and have been shown to reduce dropout as well as enhance motivation to change and reduce eating pathology in some studies [33].In spite of the growing interest in Internet-delivered interventions for women with eating disorders, there is a dearth of research in this regard, with only 8 studies having investigated predictors of treatment dropout from Internet programs aimed at women with anorexia nervosa, bulimia nervosa, or subthreshold eating disorders [6,8,15,34-38]. The commonly high levels of impulsivity in this patient group may also facilitate treatment dropout [28]. Most studies explored dropout by comparing demographic variables and symptom severity in dropouts (women who terminated treatment prematurely) and completers (women who completed treatment). Because higher symptom severity is associated with longer duration of illness, it may reflect that these patients are also more reluctant to confront their maladaptive behavior [25].
Such a group comparison approach neglects information about the time course of attrition; that is, it does not allow the comparison of different attrition curves according to possible predictors of dropout. The relationship between higher eating disorder pathology and premature termination of the online treatment program observed in the present study suggests that the online modality may be insufficient for those with more severe eating disorders.We also found that participants with higher levels of depressive mood were more likely to drop out of our program. Instead, survival analysis seems to be much more suitable for describing and testing whether and when participants drop out [39].
This finding is in accordance with previous findings from face-to-face settings for eating disorders [29,30] and a study on a Web-based treatment program for women with eating disorders [6].
An exception to this can be found in a study by Fernández-Aranda and colleagues [35] who used survival analysis to predict dropout from an Internet-based program for women with bulimia nervosa.
However, other studies have reported a lack of association between depression and dropout from Web-based programs for women with eating disorders [37,38]. It seems plausible that an impact of depression which emerges in conventional therapies could also be relevant in Web-based treatment because the contents of the Web-based treatments were the same as the contents of face-to face therapies (ie, mostly consisting of cognitive behavioral therapy and motivational interviewing; eg, [5,36]). Symptom severity at the beginning of treatment, including eating disorder pathology [15] and body dissatisfaction [6], has been shown to be positively related to dropout in some studies. However, other researchers found no differences between completers and dropouts concerning eating disorder symptoms [34,38].
A higher level of general psychopathology has been found to increase the likelihood of premature termination of Internet treatment in some studies [6,35]. One study found an effect of depressive and anxious symptoms on dropout [6], whereas 2 other studies found no such effect [37,38].Taken together, it seems that demographic variables are not reliable predictors of treatment dropout, either in face-to-face or in online settings. In contrast, symptom severity may be a more robust predictor of online treatments, but it has yielded inconclusive results in face-to-face settings [22,23].
Depression has been demonstrated to be a predictor in face-to-face settings, but not in Web-based interventions. Motivation to change is an important predictor of dropout in face-to-face settings, but it has not been studied in online interventions to date.The aim of the present study was to bridge this gap and to investigate factors leading to dropout from an anonymous Internet-delivered program to enhance motivation to change in individuals with eating disorders [13]. We investigated age, depressive mood, symptom severity, and motivation to change as predictors of dropout from this program. Given the ease of access to and termination of this program, we expected the dropout rate to be relatively high. Based on the studies described previously, we expected participants with higher depression scores, more severe eating pathology, and lower motivation to change to be more likely to terminate treatment early. We used survival analysis (ie, Cox regression) to test possible predictors of dropout from the program. Because the number of participants who dropped out was high and, accordingly, the number of events per variable was relatively low, we used the least absolute shrinkage and selection operator (LASSO) method to identify predictors.MethodsParticipantsParticipants were recruited between March 2011 and March 2012 through newspaper, magazine, and radio announcements as well as via social networks and reports on websites for people with eating disorders.
The inclusion criteria included female gender and at least one of the following eating disorder symptoms once or more per week within the past 4 weeks (assessed with the Short Evaluation of Eating Disorders [40]): purging, dieting, or excessive exercise. Participants who indicated no weight control behaviors at least once per week within the past 4 weeks and participants who reported binge eating only with the absence of any compensatory behaviors were excluded. Furthermore, those with severe depression (a score of 35 or more on the Center for Epidemiologic Studies Depression Scale, CES-D [41]), risk of suicide (assessed with items designed by the authors [13]), severe self-harming behavior (assessed with items designed by the authors [13]), psychotic disorders (a score of 13 or more on the Dutch Screening Device for Psychotic Disorder [42]), dissociative symptoms (a score greater than 8 on the Somatoform Dissociation Questionnaire [43]), substance abuse (a score of 10 or more on the Alcohol Use Disorders Identification Test [44] or the Drug Use Disorders Identification Test [45]), or in current psychotherapy treatment were excluded from the study (for more information on the inclusion and exclusion criteria, see [13]). The methods and results of the ESS-KIMO have been published elsewhere [13]; this is a secondary analysis of the program.
It is based on the transtheoretical model of behavior change [46] and uses the principles of motivational interviewing [47].

It contains 6 weekly online sessions comprising evidence-based interventions to enhance motivation to change [48,49], which are often used in conjunction with motivational interviewing. All aspects of the program and the study took place online and participants remained completely anonymous apart from providing an email address at which they could be contacted. It consisted of individual sessions, a closed website with screening for inclusion and exclusion criteria, and individualized feedback (from RvB or KH) for the required writing tasks in each session.
The content of each session (eg, information given) and the therapeutic tasks were standardized and were the same for each participant. All participants received an invitation to the next session via email 1 week after completing the previous session.
During this time, an author (RvB or KH) wrote individualized feedback for the participant’s answer to the writing task.
If participants did not log into the program during the 2 days following their invitation, a reminder email was sent on the third day. Individuals interested in participating received information about the study and were informed that they could withdraw from the study at any time. Participants also received an email address so that they could contact one of the authors (RvB or KH) if they needed additional support. The EDE-Q entails 22 items asking about eating disorder symptoms occurring within the last 28 days, with responses ranging from 0=none to 6=every day.
It consists of 4 subscales: Dietary Restraint, Eating Concerns, Weight Concerns, and Shape Concerns.
Furthermore, the EDE-Q asks about eating disorder core behaviors (eg, binge eating and purging) and their frequency during the past 28 days. It consists of 4 subscales representing the precontemplation, contemplation, action, and maintenance stage of change according to the transtheoretical model [46].
Items are rated on a 5-point Likert scale ranging from 1=disagree strongly to 5=agree strongly. Participants were asked to indicate how strongly they experienced typical depressive symptoms within the past month. They also filled in an assessment of ambivalence (the German Pros and Cons of Eating Disorders Scale [59]), a symptom-specific motivation questionnaire for eating disorders (Stages of Change Questionnaire for Eating Disorders [60]), the Rosenberg Self-Esteem Scale [61], and the Self-Efficacy Scale [62].
These questionnaires were hypothesized as outcome variables in the primary analysis of the ESS-KIMO trial. For the analysis of this study, we based our selection of variables on the literature concerning dropout from face-to-face therapies and Internet programs for women with eating disorders as well as on the restrictions because of the sample size.Definition of DropoutSeveral authors have pointed out the importance of clearly defining dropout [16,22]. Because we were interested in the predictors of treatment adherence in women with eating disorders, we only investigated dropout from treatment (treatment dropout) and not from the study (study dropout).
That is, we only included participants who had completed all baseline measurements before treatment.
Each dropout was patient-initiated; we did not recommend withdrawal from the program at any time. We defined dropout for a particular participant in a particular session when this participant completed the prior session but did not finish the current session during a 4-week time period. As recommended [39], we used survival analysis (ie, Cox regression) to test the predictors. Because 91 participants dropped out, the number of events per predictor variable (EPV) was much lower than the recommended 10 EPV [63].
Therefore, we used the LASSO to identify relevant predictors [64] because this method yields reliable estimates in such scenarios [65].
Because this method is not invariant to linear scaling, we standardized the individual variables before the analysis. The variability of the LASSO estimates is typically assessed using a simple bootstrapping approach [66]. Specifically, we drew (with replacement) 1000 pseudosamples from our full sample and calculated the LASSO. For each pseudosample, the optimal lambda was calculated by using a crossvalidation procedure with 50 folds. The smallest lambda was then chosen to calculate the LASSO estimates, and the resulting estimates were recorded. The software R was used for data analysis [67].ResultsBaseline CharacteristicsThe core demographic characteristics of participants at the beginning of the program are shown in Table 1.

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  1. GemliGiz — 03.02.2016 at 20:56:37 Ever-changing low motivation disorder nature of life other Theravada centers starting in 1975 so doing a mindfulness train is a approach.
  2. SEVIREM_SENI — 03.02.2016 at 20:50:19 Mindfulness will help enhance wellbeing (5, 6), bodily every participant was sri Huge Ashram.