Effectiveness of a Digital Cognitive Behavior Therapy–Guided Self-Help Intervention for Eating Disorders in College Women

Austin

et al. 2020

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Objective: There is a lack of research on the prevalence of eating disorders (EDs) in Singapore. The aims of our study were to (a) estimate the prevalence of EDs, risk status, and help-seeking behaviors among adults in Singapore, (b) examine gender differences between ED status groups, and (c) characterize clinical profiles of ED status groups. Method: We administered a cross-sectional survey that included a validated EDs screening tool to adults in Singapore. Results: A total of 797 Singaporean adults ages 21-77 years completed the ED screen. The majority of participants screened positive for a current DSM-5 clinical ED (6.2%), other specified feeding or ED (37.0%) or at high risk of developing an ED (19.5%). Only 1.6% of those who screened positive for an ED reported currently being in treatment. The ratio of males to females who screened positive for an ED was nearly 1:1. The clinical profiles of ED groups were consistent with the clinical presentations found in Western nations.

Section: Discussionmentioning

confidence: 99%

Estimated prevalence of eating disorders in Singapore

Chua

Austin

et al. 2020

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“…Third, only 15.5% of respondents reported initiating treatment at 2‐month follow‐up. Prior work found that 26.2% of college students screening positive for AN initiated treatment over 9 months (Fitzsimmons‐Craft et al, in press), and 28% of college students screening positive for binge‐type EDs and referred to on‐campus care sought treatment over 2 years (Fitzsimmons‐Craft et al, 2020). Current study rates were thus lower, but reported over a shorter time frame.…”

Section: Discussionmentioning

confidence: 99%

Preliminary data on help‐seeking intentions and behaviors of individuals completing a widely available online screen for eating disorders in the United States

Balantekin

Graham

et al. 2020

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Objective Scaling an online screen that provides referrals may be key in closing the treatment gap for eating disorders (EDs), but we need to understand respondents' help‐seeking intentions and behaviors after receiving screen results. This study reported on these constructs among respondents to the National Eating Disorders Association online screen who screened positive or at high risk for an ED. Method Respondents completed the screen over 18 months (February 9, 2018–August 28, 2019). Those screening positive or at high risk for an ED (n = 343,072) had the option to provide data on help‐seeking intentions (after screen completion) and behaviors (2‐month follow‐up). Results Of eligible respondents, 4.8% (n = 16,396) provided data on help‐seeking intentions, with only 33.7% of those reporting they would seek help. Only 7.6% of eligible respondents opted in to the 2‐month follow‐up, with 10.6% of those completing it (n = 2,765). Overall, 8.9% of respondents to the follow‐up reported being in treatment when they took the screen, 15.5% subsequently initiated treatment, and 75.5% did not initiate/were not already in treatment. Discussion Preliminary results suggest that among the small minority who provided data, only one‐third expressed help‐seeking intentions and 16% initiated treatment. Online screening should consider ways to increase respondents' motivation for and follow‐through with care.

“…Screen results were grouped into three ED groups: (a) no ED or risk for ARFID; (b) high risk for an ED; and (iii) clinical/subclinical AN, BN, BED, or UFED. The disorders in the clinical group reflect increased weight and shape concerns and an internalization of the thin ideal (Fairburn, 2008), and individuals with BN, BED, and UFED have been included in studies testing online interventions for EDs (Fitzsimmons‐Craft et al, 2019; Fitzsimmons‐Craft et al, 2020). In contrast, an ARFID diagnosis excludes body image disturbances (American Psychiatric Association, 2013).…”

Section: Methodsmentioning

confidence: 99%

Predicting eating disorders from Internet activity

Sadeh‐Sharvit

Taylor

et al. 2020

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Objective Eating disorders (EDs) compromise the health and functioning of affected individuals, but it can often take them several years to acknowledge their illness and seek treatment. Early identification of individuals with EDs is a public health priority, and innovative approaches are needed for such identification and ultimate linkage with evidence‐based interventions. This study examined whether Internet activity data can predict ED risk/diagnostic status, potentially informing timely interventions. Method Participants were 936 women who completed a clinically validated online survey for EDs, and 231 of them (24.7%) contributed their Internet browsing history. A machine learning algorithm used key attributes from participants' Internet activity histories to predict their ED status: clinical/subclinical ED, high risk for an ED, or no ED. Results The algorithm reached an accuracy of 52.6% in predicting ED risk/diagnostic status, compared to random decision accuracy of 38.1%, a relative improvement of 38%. The most predictive Internet search history variables were the following: use of keywords related to ED symptoms and websites promoting ED content, participant age, median browsing events per day, and fraction of daily activity at noon. Discussion ED risk or clinical status can be predicted via machine learning with moderate accuracy using Internet activity variables. This model, if replicated in larger samples where it demonstrates stronger predictive value, could identify populations where further assessment is merited. Future iterations could also inform tailored digital interventions, timed to be provided when target online behaviors occur, thereby potentially improving the well‐being of many individuals who may otherwise remain undetected.