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Abstract Background Poor quality of life in adults with anorexia nervosa (AN) and persistent high rates of readmission highlight the necessity of developing interventions to optimize treatment outcomes. ECHOMANTRA is a novel online intervention based on interventions for carers (Experienced Carers Helping Others, ECHO) and patients (Maudsley Model of Anorexia Nervosa Treatment for Adults, MANTRA) with anorexia nervosa. The objective of this paper is to describe the study protocol of a randomized control trial (RCT) aimed at evaluating the efficacy of an adaptation of the ECHOMANTRA for adults AN inpatients and outpatients, and their carers, to be implemented as an add-on to treatment-as-usual (TAU). Methods In a multi − center pilot randomized controlled trial (RCT), 148 adult AN patients, and their carers, will be randomized to receive treatment as usual (TAU) or TAU plus ECHOMANTRA. Assessments will take place at baseline (T0), post-intervention (2-month) (T1), 6-month follow-up (T2), and 12-month follow-up (T3). Primary outcomes will be eating disorder psychopathology and psychological well-being. For carers, outcome variables will include psychological well-being, accommodation and enabling behaviors, expressed emotion, illness burden, quality of life and care skills. Discussion This study will provide evidence of the efficacy of this novel, online and protocolized intervention in facilitating the recovery of these patients. Trial registration ISRCTN registry (Identifier: 80253157 https://doi.org/10.1186/ISRCTN80253157 ).

Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Each day, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, considering the availability of human and material resources, such as healthcare professionals and linear accelerators. With the increasing number of patients suffering from different types of cancers, manually establishing schedules following each patient's treatment protocols has become an extremely difficult and time-consuming task. We propose an optimization algorithm that automatically schedules and generates patient appointments. The model can rearrange fixed appointments to accommodate urgent cases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment protocols and should increase staff and patient satisfaction. The optimization algorithm can be connected to a mobile application allowing patients to accept or refuse appointment changes for rescheduling radiotherapy treatments., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.