Digital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The clinical use of digital information for diagnostic, therapeutic, and prognostic purposes has multiple patient safety problems, some of which result from poor information quality (IQ). This systematic review aims to synthesize an IQ framework that could be used to evaluate the extent to which digital health information is fit for clinical purposes. The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We searched Embase, MEDLINE, PubMed, CINAHL, Maternity and Infant Care, PsycINFO, Global Health, ProQuest Dissertations and Theses Global, Scopus, and HMIC (the Health Management Information Consortium) from inception until October 2019. Multidimensional IQ frameworks for assessing DHTs used in the clinical context by health care professionals were included. A thematic synthesis approach was used to synthesize the Clinical Information Quality (CLIQ) framework for digital health. We identified 10 existing IQ frameworks from which we developed the CLIQ framework for digital health with 13 unique dimensions accessibility, completeness, portability, security, timeliness, accuracy, interpretability, plausibility, provenance, relevance, conformance, consistency, and maintainability, which were categorized into 3 meaningful categories availability, informativeness, and usability. This systematic review highlights the importance of the IQ of DHTs and its relevance to patient safety. https://www.selleckchem.com/products/dsp5336.html The CLIQ framework for digital health will be useful in evaluating and conceptualizing IQ issues associated with digital health, thus forestalling potential patient safety problems. PROSPERO International Prospective Register of Systematic Reviews CRD42018097142; https//www.crd.york.ac.uk/prospero/display_record.php?RecordID=97142. RR2-10.1136/bmjopen-2018-024722. RR2-10.1136/bmjopen-2018-024722. Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media-based weight loss intervention. We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participantsts' psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials. Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence-powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide range of applications in health care, covering off-clinic education, health monitoring, and communication. However, conflicting factors, such as patient safety and privacy concerns, make it difficult to foresee the further development of VIPAs in health care. This study aimed to develop a plausible scenario for the further development of VIPAs in health care to support decision making regarding the procurement of VIPAs in health care organizations. We conducted a two-stage Delphi study with an internationally recruited panel consisting of voice assistant experts, medical professionals, and representatives of academia, governmental health authorities, and nonprofit health associations having expertise with voice technology. Twenty projections were formulated and evaluat in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff. According to the surveyed experts, VIPAs will show notable technological development and gain more user trust in the near future, resulting in widespread application in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff. Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This "proof-of-concept" study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotatintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation. This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.