Open Access

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasingly popular as digital interventions in a wide range of health-related applications in almost all sectors of healthcare. The surge in demand for digital medical solutions has been accelerated by the need for new diagnostic and therapeutic methods in the current coronavirus disease 2019 pandemic. This also applies to clinical practice in gastroenterology, which has, in many respects, undergone a recent digital transformation with numerous consequences that will impact patients and health care professionals in the near future. MHAs and MAs are considered to have great potential, especially for chronic diseases, as they can support the self-management of patients in many ways. Despite the great potential associated with the application of MHAs and MAs in gastroenterology and health care in general, there are numerous challenges to be met in the future, including both the ethical and legal aspects of applying this technology. The aim of this article is to provide an overview of the current status of MHA and MA use in the field of gastroenterology, describe the future perspectives in this field and point out some of the challenges that need to be addressed.

Die Kontaktpersonennachverfolgung ist derzeit eine der wirksamsten Maßnahmen zur Eindämmung der COVID-19 Pandemie. Digitales Contact Tracing mittels Smartphones scheint eine sinnvolle zusätzliche Maßnahme zur manuellen Kontaktpersonennachverfolgung zu sein, um Personen zu identifizieren, die nicht bekannt oder nicht erinnerlich sind und um den zeitlichen Verzug beim Melden eines Infektionsfalles und beim Benachrichtigen von Kontaktpersonen so gering wie möglich zu halten. Obwohl erste Modellierungsstudien eine positive Wirkung in Bezug auf eine zeitnahe Kontaktpersonennachverfolgung nahelegen, gibt es bislang keine empirisch belastbaren Daten, weder zum bevölkerungsweiten Nutzen noch zum potenziellen Schaden von Contact-Tracing-Apps. Die Beurteilung der Zweckerfüllung und eine wissenschaftliche interdisziplinäre Begleitforschung sowohl zur Wirksamkeit, Risiken und Nebenwirkungen als auch zu Implementierungsprozessen (z. B. Planung und Einbezug verschiedener Beteiligter) sind wesentliche Bestandteile einer Nutzen-Risiko Bewertung. Dieser Beitrag betrachtet daher den möglichen Public-Health-Nutzen sowie technische, soziale, rechtliche und ethische Aspekte einer Contact-Tracing-App zur Kontaktpersonennachverfolgung im Rahmen der COVID-19-Pandemie. Weiterhin werden Bedingungen für eine möglichst breite Nutzung der App aufgezeigt.

IntroductionAssessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes.MethodsWe conducted 4 weeks of multimodal sensor assessment together with real-time observation of 17 residents with moderate to very severe dementia in two nursing care units. Nursing staff received extensive training on device handling and measurement procedures. Behavior of a subsample of eight participants was further recorded by videotaping during 4 weeks during day hours. Sensors were mounted on the participants' wrist and ankle and measured motion, rotation, as well as surrounding loudness level, light level, and air pressure.ResultsParticipants were in moderate to severe stages of dementia. Almost 100% of participants exhibited relevant levels of challenging behaviors. Automated quality control detected 155 potential issues. But only 11% of the recordings have been influenced by noncompliance of the participants. Qualitative debriefing of staff members suggested that implementation of the technology and observation platform in the routine procedures of the nursing home units was feasible and identified a range of user- and hardware-related implementation and handling challenges.DiscussionOur results indicate that high-quality behavior data from real-world environments can be made available for the development of intelligent assistive systems and that the problem of noncompliance seems to be manageable. Currently, we train machine-learning algorithms to detect episodes of challenging behaviors in the recorded sensor data.

Despite the enormous number of assistive technologies (ATs) in dementia care, the management of challenging behavior (CB) of persons with dementia (PwD) by informal caregivers in home care is widely disregarded. The first-line strategy to manage CB is to support the understanding of the underlying causes of CB to formulate individualized nonpharmacological interventions. App- and sensor-based approaches combining multimodal sensors (actimetry and other modalities) and caregiver information are innovative ways to support the understanding of CB for family caregivers. The main aim of this study is to describe the design of a feasibility study consisting of an outcome and a process evaluation of a newly developed app- and sensor-based intervention to manage CB of PwD for family caregivers at home. In this feasibility study, we perform an outcome and a process evaluation with a pre-post descriptive design over an 8-week intervention period. The Medical Research Council framework guides the design of this feasibility study. The data on 20 dyads (primary caregiver and PwD) are gathered through standardized questionnaires, protocols, and log files as well as semistructured qualitative interviews. The outcome measures (neuropsychiatric inventory and Cohen-Mansfield agitation inventory) are analyzed by using descriptive statistics and statistical tests relevant to the individual assessments (eg, chi-square test and Wilcoxon signed-rank test). For the analysis of the process data, the Unified Theory of Acceptance and Use of Technology is used. Log files are analyzed by using descriptive statistics, protocols are analyzed by using documentary analysis, and semistructured interviews are analyzed deductively using content analysis. The newly developed app- and sensor-based AT has been developed and was evaluated until July in 2018. The recruitment of dyads started in September 2017 and was concluded in March 2018. The data collection was completed at the end of July 2018. This study presents the protocol of the first feasibility study to encompass an outcome and process evaluation to assess a complex app- and sensor-based AT combining multimodal actimetry sensors for informal caregivers to manage CB. The feasibility study will provide in-depth information about the study procedure and on how to optimize the design of the intervention and its delivery. DERR1-10.2196/11630