Taylorkjer2836

cts that can otherwise limit diagnostic image quality. Trial registration ClinicalTrials.gov NCT03456895; https//clinicaltrials.gov/ct2/show/NCT03456895.Background As a result of demographic changes, the number of people aged 60 years and older has been increasing steadily. Therefore, older adults have become more important as a target group for health communication efforts. Various studies show that online health information sources have gained importance among younger adults, but we know little about the health-related internet use of senior citizens in general and in particular about the variables explaining their online health-related information-seeking behavior. Media use studies indicate that in addition to sociodemographic variables, lifestyle factors might play a role in this context. Objective The aim of this study was to examine older people's health-related internet use. Our study focused on the explanatory potential of lifestyle types over and above sociodemographic variables to predict older adults' internet use for health information. Methods A telephone survey was conducted with a random sample of German adults aged 60 years and older (n=701) tes. Conclusions Our findings indicate that the internet still plays only a minor role in the health information-seeking behavior of older German adults. Nevertheless, there are subgroups including younger, more active, down-to-earth and family-oriented males that may be reached with online health information. Our findings suggest that lifestyle types should be taken into account when predicting health-related internet use behavior.Background The decline of cognitive function is an important issue related to aging. Over the last few years, numerous mobile apps have been developed to challenge the brain with cognitive exercises; however, little is currently known about how age influences capacity for performance improvement when playing cognitive mobile games. Objective The objective of this study was to analyze the score data of cognitive mobile games over a period of 100 gaming sessions to determine age-related learning ability for new cognitive tasks by measuring the level of score improvement achieved by participants of different ages. Methods Scores from 9000 individuals of different ages for 7 cognitive mobile games over 100 gaming sessions were analyzed. Scores from the first session were compared between age groups using one-way analysis of variance. Mixed models were subsequently used to investigate the progression of scores over 100 sessions. Results Statistically significant differences were found between age groups for the initial scores of 6 of the 7 games (linear trend, P less then .001). Cognitive mobile game scores increased for all participants (P less then .001) suggesting that all participants were able to improve their performance. The rate of improvement was, however, strongly influenced by the age of the participant with slower progression for older participants (P less then .001). Conclusions This study provides evidence to support two interesting insights-cognitive mobile game scores appear to be sensitive to the changes in cognitive ability that occur with advancing age; therefore, these games could be a convenient way to monitor cognitive function over long-term follow-up, and users who train with the cognitive mobile games improve regardless of age.Background Although several apps are available to support the treatment of urinary incontinence (UI), little has been reported about the experiences and preferences of their users. Objective The objective of this study was to explore the experiences and preferences of women using a mobile app for the treatment of UI and to identify potential improvements to the app. We developed this app for three types of UI stress UI, urgency UI, and mixed UI. Methods The participants in this qualitative study were women with self-reported stress UI, urgency UI, or mixed UI who used an app-based treatment to manage their condition for at least six weeks. Following the intervention, semistructured interviews were conducted to explore the participants' experiences and preferences regarding the app. All interviews were audio-recorded, transcribed verbatim, and analyzed separately by two researchers. Results Data saturation was reached after interviewing 9 women (aged 32-68 years) with stress UI (n=1, 11%), urgency UI (n=3, 33%), or mixed UI (n=5, 56%). Accessibility, awareness, usability, and adherence emerged as the main themes. On the one hand, participants appreciated that the app increased their accessibility to care, preserved their privacy, increased their awareness of therapeutic options, was easy to use and useful, and supported treatment adherence. On the other hand, some participants reported that they wanted more contact with a care provider, and others reported that using the app increased their awareness of symptoms. Selleckchem Daporinad Conclusions This qualitative study indicates that women appreciate app-based treatment for UI because it can lower barriers to treatment and increase both awareness and adherence to treatment. However, the app does not offer the ability of face-to-face contact and can lead to a greater focus on symptoms.Background Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration-approved immune checkpoint inhibitors. Methods In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary.