Problematic usage of the internet and eating disorder and related psychopathology: A multifaceted, systematic review and meta-analysis.

Eating disorders are widespread illnesses with significant impact. There is growing concern about how those at risk of eating disorders overuse online resources to their detriment. We conducted a pre-registered systematic review and meta-analysis of studies examining Problematic Usage of the Internet (PUI) and eating disorder and related psychopathology. The meta-analysis comprised n = 32,295 participants, in which PUI was correlated with significant eating disorder general psychopathology Pearson r = 0.22 (s.e. = 0.04, p < 0.001), body dissatisfaction r = 0.16 (s.e. = 0.02, p < 0.001), drive-for-thinness r = 0.16 (s.e. = 0.04, p < 0.001) and dietary restraint r = 0.18 (s.e. = 0.03). Effects were not moderated by gender, PUI facet or study quality. Results are in support of PUI impacting on eating disorder symptoms; males may be equally vulnerable to these potential effects. Prospective and experimental studies in the field suggest that small but significant effects exist and may have accumulative influence over time and across all age groups. Those findings are important to expand our understanding of PUI as a multifaceted concept and its impact on multiple levels of ascertainment of eating disorder and related psychopathology.

Early warning systems in inpatient anorexia nervosa: A validation of the MARSIPAN-based modified early warning system.

OBJECTIVE: We aimed to evaluate the validity of a MARSIPAN-guidance-adapted Early Warning System (MARSI MEWS) and compare it to the National Early Warning Score (NEWS) and an adapted version of the Physical Risk in Eating Disorders Index (PREDIX), to ascertain whether current practice is comparable to best-practice standards. METHODS: We collated 3,937 observations from 36 inpatients from Addenbrookes Hospital over 2017-2018 and used three independent raters to create a "gold standard" of deteriorating cases. We ascertained performance metrics (Receiver Operating Characteristic Area Under the curve) for MARSI MEWS, NEWS and PREDIX; we also tested the proof of concept of a machine-learning-based early-warning-system (ML-EWS) using cross-validation and out-of-sample prediction of cases. RESULTS: The MARSI MEWS system showed higher ROC AUC (0.916) compared to NEWS (0.828) or PREDIX (0.865). ML-EWS (random forest) performed well at independent samples analysis (0.980) and multilevel analysis (0.922). CONCLUSION: MARSI MEWS seems most suitable for identifying critically deteriorating cases in anorexia nervosa inpatient population. We did not examine community practice in which the PREDIX arguably remains the best to ascertain deteriorating cases. Our results also provide a first proof of concept for the development of artificial-intelligence-based early warning systems in anorexia nervosa. Implications for inpatient clinical practice in eating disorders are discussed.