UNASSIGNED: To provide an overview of the digital mental health care landscape for individuals with spinal cord injury (SCI).
UNASSIGNED: PubMed, PsycInfo, and PSYNDEX were searched for articles meeting the following criteria: (1) article written in English or German; (2) digital psychosocial intervention; (3) SCI only; (4) treatment of individuals with SCI and not their relatives or caregivers. Records were screened by title and abstract and records meeting the inclusion criteria were obtained for full text screening. The references of identified articles were screened to find further relevant articles. The literature search was updated before submission. Risk of Bias was assessed by using the Cochrane risk-of-bias tool for randomized trials (RoB 2) and a narrative synthesis was conducted.
UNASSIGNED: Ten randomized-controlled trials (RCT) and ten non-randomized-controlled trials were identified and compared in this review, evaluating twelve internet- and mobile-based interventions, five smartphone apps, and three virtual reality applications. The interventions were primarily used as stand-alone aftercare programs. While some were not based on any theory, cognitive behavioral therapy mostly served as the theoretical basis for the online interventions. The extent of human support also varied greatly between the studies. The number of intervention modules ranged between 2 and 72. There were also major differences in outcome variables and effects. A meta-analytical evaluation of the data was not conducted due to heterogeneity of studies.
UNASSIGNED: Digital applications to promote the psychosocial health of individuals with SCI are an emerging field of research with many treatment approaches still to come. First high quality RCT studies report promising results. Unfortunately, not all studies are of high quality or the interventions have been insufficiently adapted to the needs of people with SCI. Therefore, more research is needed to further develop applications, and to generalize and test the effects found in the long term.

UNASSIGNED: Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies.
UNASSIGNED: The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review.
UNASSIGNED: As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users.
UNASSIGNED: This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.

BACKGROUND: Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), metabolic syndrome as well as cardiovascular diseases. Against this background, mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM. Therefore, we analyzed the clinical effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes in mother and child.
METHODS: A systematic literature search in Medline (PubMed), Cochrane Library, Embase, CINAHL and Web of Science Core Collection databases as well as Google Scholar was performed. We selected studies published 2008 to 2020 analyzing women diagnosed with GDM using specific mHealth-Apps. Controlled clinical trials (CCT) and randomized controlled trials (RCT) were included. Study quality was assessed using the Effective Public Health Practice Project (EPHPP) tool.
RESULTS: In total, n = 6 publications (n = 5 RCTs, n = 1 CCT; and n = 4 moderate, n = 2 weak quality), analyzing n = 408 GDM patients in the intervention and n = 405 in the control groups, were included. Compared to control groups, fasting blood glucose, 2-h postprandial blood glucose, off target blood glucose measurements, delivery mode (more vaginal deliveries and fewer (emergency) caesarean sections) and patient compliance showed improving trends.
CONCLUSIONS: mHealth-Apps might improve health-related outcomes, particularly glycemic control, in the management of GDM. Further studies need to be done in more detail.

According to the World Health Organization, the worldwide prevalence of diabetes mellitus (DM) is increasing dramatically and DM comprises a large part of the global burden of disease. At the same time, the ongoing digitalization that is occurring in society today offers novel possibilities to deal with this challenge, such as the creation of mobile health (mHealth) apps. However, while a great variety of DM-specific mHealth apps exist, the evidence in terms of their clinical effectiveness is still limited.
The objective of this review was to evaluate the clinical effectiveness of mHealth apps in DM management by analyzing health-related outcomes in patients diagnosed with type 1 DM (T1DM), type 2 DM (T2DM), and gestational DM.
A scoping review was performed. A systematic literature search was conducted in MEDLINE (PubMed), Cochrane Library, EMBASE, CINAHL, and Web of Science Core Collection databases for studies published between January 2008 and October 2020. The studies were categorized by outcomes and type of DM. In addition, we carried out a meta-analysis to determine the impact of DM-specific mHealth apps on the management of glycated hemoglobin (HbA1c).
In total, 27 studies comprising 2887 patients were included. We analyzed 19 randomized controlled trials, 1 randomized crossover trial, 1 exploratory study, 1 observational study, and 5 pre-post design studies. Overall, there was a clear improvement in HbA1c values in patients diagnosed with T1DM and T2DM. In addition, positive tendencies toward improved self-care and self-efficacy as a result of mHealth app use were found. The meta-analysis revealed an effect size, compared with usual care, of a mean difference of -0.54% (95% CI -0.8 to -0.28) for T2DM and -0.63% (95% CI -0.93 to -0.32) for T1DM.
DM-specific mHealth apps improved the glycemic control by significantly reducing HbA1c values in patients with T1DM and T2DM patients. In general, mHealth apps effectively enhanced DM management. However, further research in terms of clinical effectiveness needs to be done in greater detail.