AN is the third leading cause of chronic illness in adolescents and, if left untreated, can lead to major organ damage, structural and functional brain changes, and early-onset osteopenia and osteoporosis. Although family-based interventions are efficacious for up to 75% of adolescents with AN, approximately 30% will relapse after recovery. There is a critical need to optimize treatments and prevent post-discharge relapse following acute treatment to improve outcomes for adolescents with AN. To address this critical need, the investigative team developed a suite of digital tools that advance the science of assessment, risk prediction, and clinical-decision support for use in the post-acute treatment window, called "Smart Treatment for Anorexia Recovery (STAR)." STAR uses cutting-edge assessment technology to shorten test administration and machine-learning to predict likelihood of recovery. This information is then provided back to the clinician via an easy-to-use clinical-decision support tool to alert the clinician when user-entered data suggests the patient is not progressing.

In the current clinical trial, the investigators will expand STAR to test an adaptive mHealth intervention delivered in the post-discharge window. The investigators' hypothesis is that a transdiagnostic assessment and clinical-decision support tool delivered within the STAR suite will optimize face-to-face clinical service and the addition of an adaptive mobile-health (mHealth) intervention will improve outpatient treatment response and reduce relapse in adolescents discharged from intensive treatment for AN. The investigators' previous work supports the hypothesis. Specifically, the investigators' past studies provide robust support for the predictive validity and clinical utility of the investigators' assessment tool for predicting ED-related psychiatric impairment and recovery. However, the number of items across the paper-based assessment tool is 144, which is overly long for routine use. To overcome this challenge, the investigators developed a mobile phone app that uses computerized adaptive testing to reduce assessment length by up to 50% while retaining the reliability and validity of the original paper-and-pencil measure. The investigators propose to leverage this innovation to optimize both face-to-face and mHealth treatment for AN. The objectives are to: 1) develop the mHealth intervention (with clinician and stakeholder input) and 2) establish feasibility, acceptability, and preliminary effect size of the mHealth intervention using both clinician and patient data. To accomplish these objectives, the investigators will employ a computerized adaptive test coupled with machine learning algorithms, delivered within the app to signal clinicians when their clients are at-risk for poor outcomes and relapse. Specific aims include: 1) adapt the existing clinical tool to provide therapist support modules and patient mHealth messages; 2) conduct a preliminary randomized controlled trial (RCT) of the integrated assessment and mHealth intervention tool; 3) test preliminary mechanisms that lead to changes in AN symptoms.

STAR incorporates elements from the Unified Protocol and Acceptance and Commitment Therapy to target emotion avoidance, which the investigators hypothesize will lead to reductions in eating-disorder behaviors. The investigators will compare STAR to Present-focused Anorexia Nervosa Coping Treatment (PACT), which focuses on current life stressors and problems. The investigators' hypothesis is that STAR will improve outpatient treatment response and reduce relapse in adolescents discharged from intensive treatment for AN. The investigators will evaluate hypotheses in two conditions: 1) STAR app and 2) PACT app. In both conditions, participants must be working with an individual outpatient therapist.

Given there is a scarcity of specialty care for AN following acute treatment, yet 95% of adolescents have smartphones, the proposed research is innovative and significant because it has the future potential to reduce relapse and optimize existing community-delivered interventions for AN over the post-acute treatment window.