BackgroundNovel interventions are needed to improve lifestyle and prevent noncommunicable diseases, the leading cause of death and disability globally. This study aimed to systematically review, synthesize, and grade scientific evidence on effectiveness of novel information and communication technology to reduce noncommunicable disease risk.Methods and ResultsWe systematically searched PubMed for studies evaluating the effect of Internet, mobile phone, personal sensors, or stand‐alone computer software on diet, physical activity, adiposity, tobacco, or alcohol use. We included all interventional and prospective observational studies conducted among generally healthy adults published between January 1990 and November 2013. American Heart Association criteria were used to evaluate and grade the strength of evidence. From 8654 abstracts, 224 relevant reports were identified. Internet and mobile interventions were most common. Internet interventions improved diet (N=20 studies) (Class IIa A), physical activity (N=33), adiposity (N=35), tobacco (N=22), and excess alcohol (N=47) (Class I A each). Mobile interventions improved physical activity (N=6) and adiposity (N=3) (Class I A each). Evidence limitations included relatively brief durations (generally <6 months, nearly always <1 year), heterogeneity in intervention content and intensity, and limited representation from middle/low‐income countries.ConclusionsInternet and mobile interventions improve important lifestyle behaviors up to 1 year. This systematic review supports the need for long‐term interventions to evaluate sustainability.

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Objective: To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms. Design: Multicenter retrospective cohort study. Setting: The study included 43 hospitals using a common infection prevention surveillance system. Methods: A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods. Results: We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work. Conclusions: Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.