Only one high-quality study, specifically focused on the effects of auditory rhythmical cueing, suggesting that the walking speed of patients with Parkinson's disease can be positively influenced. However, it is unclear whether positive effects identified in the laboratory can be generalized to improved activities of daily living (ADLs) and reduced frequency of falls in the community. In addition, the sustainability of a cueing training programme remains uncertain.

Cytology has the best specificity and telomerase the best sensitivity. However, none of the markers studied here is sensitive enough to be recommended for daily routine.

Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.

Most patients reported combined incontinence (59%) and underlying pathophysiologic abnormalities were identified. The hypothesized associations between urge and passive incontinence and functional and anatomical impairment of the anorectum are less clear-cut than previously assumed. Patients presenting with fecal incontinence should undergo physiologic investigation.

Anal inspection and digital rectal examination can give accurate information about internal and external anal sphincter function but are inaccurate for determining external anal sphincter defects <90 degrees. Therefore, a sufficient diagnostic work-up should comprise at least rectal examination, anal inspection and endoanal ultrasonography.