Examining the dissociative basis for body image disturbances

Although dissociative symptoms have been linked with both food- and appearance-related aspects of eating disorders, the psychological mechanisms underlying these relationships remain unclear. The present study evaluated the hypothesis that the disturbances of self-identity attributed to dissociation can manifest as disturbances of body image and, in turn, undermine body-specific self-evaluations relevant to disordered eating (i.e., body comparison, body dissatisfaction, and internalization of the thin ideal). Ninety-three female university students completed self-report measures of dissociation and body-related aspects of disordered eating. In addition, the method of constant stimuli was used to experimentally derive three measures of body image disturbance: (1) accuracy of body size estimations (body image distortion), (2) ability to discriminate between different body sizes (body image sensitivity), and (3) consistency in one&rsquo;s body size estimations (body image variability). The findings show that dissociation is related to symptoms of disordered eating, and that these relationships may be mediated by body image instability. Collectively, these findings support the notion that the body image attitudes and behaviours that characterize eating disorders may derive from proprioceptive deficits due to dissociation.<br /

Australian healthcare professionals\u27 knowledge of and attitudes toward binge eating disorder

Objective: This study aimed to investigate Australian healthcare practitioners&rsquo; knowledge and attitudes toward binge eating disorder (BED).Method: Participants were 175 healthcare professionals, who were randomized to one of two conditions that assessed diagnostic and treatment knowledge of either comorbid BED and obesity or only obesity via case vignette, as well as weight bias toward obese patients.Results: Results suggested that participants demonstrated a reluctance to diagnose comorbid BED and obesity, that their knowledge of physical complications associated with BED was limited, and that they indicated a narrow range of evidence-based treatment options. When compared with levels of weight bias expressed by healthcare professionals in previous international studies, Australian clinicians were significantly less biased, however, still largely endorsed &lsquo;negative&rsquo; attitudes toward obesity.Conclusion: Findings suggest that future clinical training in eating disorders should therefore focus not only on diagnostic criteria, physical complications and treatment options, but also on practitioner attitudes toward eating and weight

The work readiness scale (WRS): developing a measure to assess work readiness in college graduates

Work readiness is a relatively new concept which has emerged in the literature as a selection criterion for predicting graduate potential. Its definition and validity however, is contentious. To address this issue, the current study aimed to identify the attributes and characteristics that comprise work readiness and develop a scale to assess graduate work readiness. A qualitative study was conducted to assist in generating a representative pool of items for quantitative measurement. The resultant 167 item Work Readiness Scale (WRS) which we developed was validated in a sample of 251 graduates across a range of disciplines. Item analysis assisted in refining the scale. Exploratory factor analyses supported a 4-factor solution, with the final WRS consisting of 64 items. The four factors explained 44.7% of the variance, demonstrated excellent reliability and were labelled personal characteristics, organisational acumen, work competence, and social intelligence. The findings indicate that work readiness is a multidimensional construct and initial evidence is provided for the construct validity of the WRS

Body image during pregnancy: an evaluation of the suitability of the body attitudes questionnaire

Background:&nbsp;Available data suggest that body dissatisfaction is common during pregnancy and may even be a&nbsp;precursor to post-natal depression. However, in order to accurately identify at-risk women, it is essential to first&nbsp;establish that body image measures function appropriately in pregnant populations. Our study examines the&nbsp;suitability of the Body Attitudes Questionnaire (BAQ) for measuring body dissatisfaction among pregnant women&nbsp;by comparing the psychometric functioning of the BAQ: (1) across key phases of pregnancy, and (2) between&nbsp;pregnant and non-pregnant women.&nbsp;Methods: A total of 176 pregnant women from Melbourne, Victoria filled out a questionnaire battery containing&nbsp;demographic questions and the Body Attitudes Questionnaire at 16, 24, and 32 weeks during pregnancy. A&nbsp;comparison group of 148 non-pregnant women also completed the questionnaire battery at Time 1. Evaluations of&nbsp;the psychometric properties of the BAQ consisted of a series of measurement invariance tests conducted within a&nbsp;structural equation modelling framework.Results: Although the internal consistency and factorial validity of the subscales of the BAQ were established across&nbsp;time and also in comparisons between pregnant and non- pregnant women, measurement invariance tests showed&nbsp;non-invariant item intercepts across pregnancy and also in comparison with the non-pregnant subgroup.&nbsp;Inspection of modification indices revealed a complex, non-uniform pattern of differences in item intercepts across&nbsp;groups.Conclusions: Collectively, our findings suggest that comparisons of body dissatisfaction between pregnant and&nbsp;non-pregnant women (at least based on the BAQ) are likely to be conflated by differential measurement biases that serve to undermine attempts to accurately assess level of body dissatisfaction. Researchers should be cautious in&nbsp;assessments of body dissatisfaction among pregnant women until a suitable measure has been established for use&nbsp;in this population. Given the fact that body dissatisfaction is often associated with maladaptive behaviours, such as&nbsp;unhealthy eating and extreme weight loss behaviours, and with ante-and post-natal depression, that have serious&nbsp;negative implications for women&rsquo;s health and well-being, and potentially also for the unborn foetus during&nbsp;pregnancy, developing a suitable body image screening tool, specific to the perinatal period is clearly warranted.</div

A broad v. focused digital intervention for recurrent binge eating: a randomized controlled non-inferiority trial

Background: Empirically validated digital interventions for recurrent binge eating typically target numerous hypothesized change mechanisms via the delivery of different modules, skills, and techniques. Emerging evidence suggests that interventions designed to target and isolate one key change mechanism may also produce meaningful change in core symptoms. Although both ‘broad’ and ‘focused’ digital programs have demonstrated efficacy, no study has performed a direct, head-to-head comparison of the two approaches. We addressed this through a randomized non-inferiority trial. Method: Participants with recurrent binge eating were randomly assigned to a broad (n = 199) or focused digital intervention (n = 199), or a waitlist (n = 202). The broad program targeted dietary restraint, mood intolerance, and body image disturbances, while the focused program exclusively targeted dietary restraint. Primary outcomes were eating disorder psychopathology and binge eating frequency. Results: In intention-to-treat analyses, both intervention groups reported greater improvements in primary and secondary outcomes than the waitlist, which were sustained at an 8-week follow-up. The focused intervention was not inferior to the broad intervention on all but one outcome, but was associated with higher rates of attrition and non-compliance. Conclusion: Focused digital interventions that are designed to target one key change mechanism may produce comparable symptom improvements to broader digital interventions, but appear to be associated with lower engagement

Social media markers to identify fathers at risk of postpartum depression : a machine learning approach

Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

Objective: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. Method: Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). Results: The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48–0.52), but adequate for symptom-level change (R2 =.15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC