AMICAI: A Method Based on Risk Analysis to Integrate Responsible Research and Innovation into the Work of Research and Innovation Practitioners.

The integration of ethics into the day-to-day work of research and innovation (R&I) is an important but difficult challenge. However, with the Aachen method for identification, classification and risk analysis of innovation-based problems (AMICAI) an approach from an engineering perspective is presented that enables the integration of ethical, legal and social implications into the day-to-day work of R&I practitioners. AMICAI appears in particular capable of providing a procedural guidance for R&I practitioners based on a method established in engineering science, breaking down the object of consideration into partial aspects and prioritizing the innovation-based problems in dependence of potential risk. This enables the user to apply AMICAI continuously during all stages of the research and development (R&D) process and to analyze and choose between certain sociotechnical alternatives. In this way, problems that affect ethical, legal, and social aspects can be understood, reflected and considered in the mostly technically focused R&D process. The paper gives a general guidance about AMICAI by describing principles and assumptions, providing the steps of analysis and application aids, giving an example application, explaining the necessary adjustments of AMICAI compared to the methodical basis of failure mode, effects, and criticality analysis and discussing the advantages and limits. AMICAI's simple applications can stimulate interdisciplinary cooperation in the R&D process and be a starting point for the development of an "open RRI risk analysis platform" allowing society to evaluate innovation-based problems.

Survey-based personas for a target-group-specific consideration of elderly end users of information and communication systems in the German health-care sector.

BACKGROUND: In many countries, including Germany, older people are increasing in numbers, while fewer caregivers are available. A way to address the problem is to develop new medical assistance and monitoring systems that are operated by the elderly on their own, e.g. in-home aftercare systems. OBJECTIVE: The development of a set of eight data-based personas in terms of a best practice approach is presented. METHOD: "Personas" are an integral method of the user-centered design approach. They address the problem of incomplete knowledge of individual user behaviour by introducing archetypal user groups. Thus, personas can be used at an early stage of development to raise the awareness of developers to the needs, skills, and abilities of the elderly. Personas are also a cost-effective method and quickly and easily accessible. In order to guarantee representativeness the development of personas needs to occur based on a robust data set of a certain user group. RESULTS: This article presents the data-driven development of eight personas. The applied data set results from a nationwide questionnaire study on the elderly's use of information and communication technology, out of elderly people in Germany. The results will be presented in terms of best practice. CONCLUSION: To conclude, survey-based personas of older end users can play an important role in the research and development of innovative devices. APPLICATION: The personas presented in this paper can be used in research and development to raise awareness of the needs and demands of end users.

Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults.

BACKGROUND: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists. OBJECTIVE: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization. METHODS: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations. RESULTS: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ24=14.1, P=.002) and monitoring (χ24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for "monitoring" between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. CONCLUSIONS: We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine-chosen as the basis for the construction of present taxonomy-was confirmed.

Prevalence of Health App Use Among Older Adults in Germany: National Survey.

BACKGROUND: Health 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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: In 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. CONCLUSIONS: Health 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 "Aachen fall prevention App" - a Smartphone application app for the self-assessment of elderly patients at risk for ground level falls.

BACKGROUND: Fall 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. RESULTS: The 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. CONCLUSIONS: The "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.