The human body is more fragile than people think. In order to survive, it requires sleep just as much as food, water, or oxygen. This is a basic principle of human physiology that has been borne out by thousands of research studies.In general, sleep is a state where our bodies and minds rest and rejuvenate (Spriggs 2009). It is obligatory for our normal physiological, mental and emotional functioning during awake hours. The belief that it is possible to have just a couple of hours of sleep a night over a long period of time without suffering any negative consequences is a common misconception (National Heart Lung and Blood Institute (NHLBI) 2011). It is categorically beyond doubt that when sleep contains even slight abnormalities, the aftermath can lead to physical illness, psychological problems or an untimely death (Lee-Chiong et al 2012).A popular misconception is that adults have to sleep at least 7-8 h every night to be rejuvenated properly, while children require far more hours of sleep (National Heart Lung and Blood Institute (NHLBI) 2011). However, this is only standard recommended advice; sleep requirements are individual for every person (National Heart Lung and Blood Institute (NHLBI) 2011). In addition, getting many hours of sleep does not always guarantee a healthy and rested state, because the crucial point here is not the quantity, but the quality (National Heart Lung and Blood Institute (NHLBI) 2011).

Purpose-The purpose of this paper is to find out tourism movement patterns via the tracking of tourists with the help of positioning systems like GPS in the rural area of the Lake Constance destination in Germany. In doing so past, present and future of tourist tracking is illustrated. Design/methodology/approach-The tracking is realized via common smartphones extended by an app, with dedicated sensors like position loggers and a survey. The three different approaches are applied in order to compare and cross-check results (triangulation of data and methods). Findings-Movement patterns turned out to be diverse and individualistic within the rural destination of Lake Constance and following an ants trail in sub-destinations like the city of Constance. Repeat visitors and firsttime visitors alike always visit the bigger cities and main day-trip destinations of the Lake. A possible prediction tool enables new avenues of governing tourism movement patterns. Research limitations/implications-The tracking techniques can be developed further into the direction of "quantified self" using gamification in order to make the tracking app even more attractive. Practical implications-An algorithm-based prediction tool would offer new perspectives to the management of tourism movements. Social implications-Further research is needed to overcome the feeling of invasiveness of the app to allow tracking with that approach. Originality/value-This study is original and innovative because of the first-time use of a smartphone app in tourist tracking, the application on a rural destination and the conceptual description of a prediction tool.

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Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive. Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes. We present the literature review of non-invasive sound acquisition devices and techniques. The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope. Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

Background One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment. Objective This paper aims to review the feasibility of blockchain technology for telemedicine. Methods The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex). Results Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%). Conclusions These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.

To evaluate the quality of sleep, it is important to determine how much time was spent in each sleep stage during the night. The gold standard in this domain is an overnight polysomnography (PSG). But the recording of the necessary electrophysiological signals is extensive and complex and the environment of the sleep laboratory, which is unfamiliar to the patient, might lead to distorted results. In this paper, a sleep stage detection algorithm is proposed that uses only the heart rate signal, derived from electrocardiogram (ECG), as a discriminator. This would make it possible for sleep analysis to be performed at home, saving a lot of effort and money. From the heart rate, using the fast Fourier transformation (FFT), three parameters were calculated in order to distinguish between the different sleep stages. ECG data along with a hypnogram scored by professionals was used from Physionet database, making it easy to compare the results. With an agreement rate of 41.3%, this approach is a good foundation for future research.

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