State-wide university implementation of an online platform for eating disorders screening and intervention.

Abstract

The Internet-based Healthy Body Image (HBI) Program platform uses online screening to identify individuals at low risk for, high risk for, or with an eating disorder (ED) and then directs users to tailored, evidence-based online/mobile interventions or referral to in-person care to address individuals' risk/clinical status. We examined findings from the first state-wide deployment of HBI over the course of 3 years in Missouri public universities, sponsored by the Missouri Eating Disorders Council and the Missouri Mental Health Foundation. First, the screen was completed 2,454 times, with an average of 2.5% of the undergraduate student body on each campus taking the screen. Second, ED risk level in the participating students was high-over 56% of students screened were identified as being at high risk for ED onset or having a clinical/subclinical ED. Third, uptake for the HBI online/mobile interventions ranged from 44 -51%, with higher rates of uptake in the high-risk compared with low-risk group. Fourth, results showed that, for students with a clinical/subclinical ED, use of the clinical mobile application Student Bodies-Eating Disorders intervention resulted in significantly decreased restrictive eating and binge eating. Neither vomiting nor diet pill/laxative use was found to decrease, but reports of these behaviors were very low. This is the first deployment of a comprehensive online platform for screening and delivering tailored interventions to a population of individuals with varying ED risk and symptom profiles in an organized care setting. Implications for future research and sustaining and broadening the reach of HBI are discussed.

Results of disseminating an online screen for eating disorders across the U.S.: Reach, respondent characteristics, and unmet treatment need

“…Screen respondents in the current study self‐selected to complete the online screening tool, which likely explains the much greater level of eating disorder pathology in this sample. In our work implementing an online platform for eating disorders screening and intervention across public universities in the state of Missouri in the U.S. (Fitzsimmons‐Craft et al, ) and as part of a randomized controlled trial in 28 geographically diverse U.S. universities (Fitzsimmons‐Craft et al, ), we similarly demonstrated a high level of eating disorder risk in those who chose to participate in the online screen. This is a key finding as it suggests the current screen attracts respondents with elevated eating disorder pathology and thus, when disseminated widely, has the capability of increasing detection and access to care for individuals at risk for or with eating disorders.…”

“…Responses are used to categorize individuals into one of seven possible DSM‐5 diagnoses or two risk categories: (1) anorexia nervosa (AN); (2) bulimia nervosa (BN); (3) binge eating disorder (BED); (4) subclinical bulimia nervosa (sub‐BN); (5) subclinical binge eating disorder (sub‐BED); (6) unspecified feeding or eating disorder (UFED); (7) avoidant/restrictive food intake disorder (ARFID); (8) high risk for an eating disorder; or (9) not at risk for an eating disorder. The SWED screening algorithm was validated in college‐age women, with sensitivities ranging from 0.68 (sub‐BN) to 0.90 (AN) and specificities ranging from 0.79 (sub‐BED) to 0.99 (AN) compared to diagnostic interview (see Graham et al, for additional information), and has been used in past research among both men and women (Fitzsimmons‐Craft et al, ). The question, “Are you currently in treatment for an eating disorder?” (with response options of: “no”; “yes”; and “not currently, but in the past” provided) was used to assess eating disorder treatment status.…”

User‐centered design for technology‐enabled services for eating disorders

“…Reach is defined as the number of people who are offered a service relative to the number of people eligible to receive it, and engagement is defined as uptake and/or adherence to a service. For example, in two initiatives we conducted to implement online screening and intervention for eating disorders on university campuses in the U.S., <3% of the student body completed screening (Fitzsimmons‐Craft et al, ; Fitzsimmons‐Craft et al, ). Similar results were observed for an Internet‐based eating disorder intervention delivered in an Irish university setting (Lindenberg, Moessner, Harney, McLaughlin, & Bauer, ).…”

Optimizing eating disorder treatment outcomes for individuals identified via screening: An idea worth researching

“…To improve uptake, a research team would identify the optimization criterion. For instance, Fitzsimmons‐Craft, Firebaugh, et al () found 50% uptake of digital programs among a university sample following an online screen; surpassing this number could be the target. Then, strategies would be tested that address factors impacting uptake, like varying the type of feedback users receive following screening.…”

“…Despite the successes in identifying individuals with EDs in need of intervention, gaps remain in optimizing outcomes for these individuals: (a) screens do not reach all of those in need; (b) there is a large treatment gap between the number of individuals in need of and who receive treatment for an ED (Hart, Granillo, Jorm, & Paxton, ); (c) the number of individuals who follow through on treatment recommendations after a screen is suboptimal (DeBar et al, ); and (d) among those who do receive treatment, many individuals fail to complete a full course of treatment or achieve a desired outcome within a set time frame (Bauer & Moessner, ; Eisenberg et al, ; Fitzsimmons‐Craft, Balantekin, Eichen, et al, ; Fitzsimmons‐Craft, Balantekin, Graham, et al, ; Fitzsimmons‐Craft, Eichen, et al, ; Fitzsimmons‐Craft, Firebaugh, et al, ; Kazdin, Fitzsimmons‐Craft, & Wilfley, ; Lindenberg, Moessner, Harney, McLaughlin, & Bauer, ).…”