BackgroundHealth apps are increasingly becoming an integral part of health care. Especially in older adults, the self-management of chronic diseases by health apps might become an integral part of health care services.ObjectiveThe aim of this explorative study was to investigate the prevalence of health app use and related demographic factors, as well as health status among older adults in Germany.MethodsA nationwide postal survey was conducted. Of the 5000 individuals contacted, a total of 576 participants completed this survey. On the basis of their self-indicated assignment to one of the three predefined user groups (health app users, general app users, and nonusers of apps), participants answered various questions regarding app and health app use, including frequency of use and number of installed apps, demographic factors, and health status.ResultsIn total, 16.5% (95/576) used health apps, whereas 37.5% (216/576) indicated only using general apps, and 46.0% (265/576) reported using no apps at all. The number of installed health apps was most frequently reported as between 1 and 5 apps per participant, which were usually used on a weekly basis. The most frequently cited type of health apps were exercise-related ones. Individuals using health apps were found to be younger (MeanmHealth 66.6, SD 4.7) and to have a higher level of technical readiness compared with general app users and nonusers of apps (adjusted odds ratio, AOR=4.02 [95% CI 2.23-7.25] for technical readiness, and AOR=0.905 [95% CI 0.85-0.97] for age). The most frequently mentioned sources of information about apps within the group of health and general app users were family and friends. Identified barriers against the use of health apps were a lack of trust, data privacy concerns, and fear of misdiagnosis.ConclusionsHealth apps are already used by older adults in Germany. The main type of apps used are exercise-related ones. Barriers to and incentives for the use of health apps and associations with health status and users’ demographics were revealed.

The use of robots in the national economy-especially in industrialized countries-is growing. At the same time, the interdependency between humans and robots is getting increasingly closer: they are engaging in direct contact with each other as more and more organizations let robots and humans work hand-in-hand. One factor that predicts successful human-robot interdependency is the acceptance of the robot by the human. Generally, only when an innovative assistive working system covers human needs and expectations, it is perceived to be useful and hence accepted. Furthermore, it has been found that cultural context has an impact on human-robot interaction, as people feel more comfortable interacting with a robot in a culturally normative way. Therefore this paper aims at presenting a human-robot collaboration acceptance model (HRCAM) with regard to the collaboration between humans and robots that is based on prior acceptance models, while also considering technology affinity and ethical, legal and social implications. Additionally, similarities and differences in robot acceptance are shown for four selected countries-both in comparison to the overall human-robot collaboration acceptance model and between the countries. The HRCAM additionally shows which variables influence perceived usefulness and perceived ease of use, and thus behavioral intention to use and use behavior. A further distinction is made between anchor variables, which can be influenced in the long term, and adjustment variables, which can be influenced in the short to medium term. The model therefore offers practitioners in the field of human-robot collaboration recommendations to increase the acceptance of robots. Keywords Technology acceptance • Human-robot interaction • Human-robot cooperation • Human-machine interaction • Cross-cultural differences • TAM

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BackgroundFall incidents are a major problem for patients and healthcare. The “Aachen Fall Prevention App” (AFPA) represents the first mobile Health (mHealth) application (app) empowering older patients (persons 50+ years) to self-assess and monitor their individual fall risk. Self-assessment is based on the “Aachen Fall Prevention Scale,” which consists of three steps. First, patients answer ten standardized yes–no questions (positive criterion ≥ 5 “Yes” responses). Second, a ten-second test of free standing without compensatory movement is performed (positive criterion: compensatory movement). Finally, during the third step, patients rate their subjective fall risk on a 10-point Likert scale, based on the results of steps one and two. The purpose of this app is (1) to offer a low-threshold service through which individuals can independently monitor their individual fall risk and (2) to collect data about how a patient-centered mHealth app for fall risk assessment is used in the field.ResultsThe results represent the first year of an ongoing field study. From December 2015 to December 2016, 197 persons downloaded the AFPA (iOS™ and Android™; free of charge). N = 111 of these persons voluntarily shared their data and thereby participated in the field study. Data from a final number of n = 79 persons were analyzed due to exclusion criteria (age, missing objective fall risk, missing self-assessment). The objective fall risk and the self-assessed subjective risk measured by the AFPA showed a significant positive relationship.ConclusionsThe “Aachen Fall Prevention App” (AFPA) is an mHealth app released for iOS and Android. This field study revealed the AFPA as a promising tool to raise older adults’ awareness of their individual fall risk by means of a low-threshold patient-driven fall risk assessment tool.

BACKGROUND: Information and communication technology increasingly addresses the information needs patients have regarding their personal health. While an understanding of older adults' needs is crucial for developing successful eHealth technology, user research results hardly apply to different systems. OBJECTIVE: The present study aims at: (1) describing and analysing the context of digital health systems in a general manner, (2) investigating if information need of older adults influences their technology usage to show the relevance of the concept for a general context analysis and (3) testing which demographic variables intervene with their health information need. METHODS: Survey data from a longitudinal study with older adults (N = 551) were reported descriptively. After showing a significant relationship during chi-square tests, we quantified the ones between general health information need and technology usage, as well as between general health information need and the demographic variables age, education, chronic diseases and gender by means of (multiple) linear regression models. RESULTS: We predicted older adults' technology usage based on their health information need. The results confirmed this relationship. Higher information need led to a more frequent usage of apps installed on the tablet personal computer (PC), to a frequent use of smartwatches and to the possession of a computer or laptop. Users' education has a higher impact on health information need than amount of chronic diseases, gender and age. CONCLUSIONS: Information need emerged as a useful object for investigation of context and user requirement analysis across different systems: it predicted technology usage so that design recommendations derived from the descriptive gained in importance.