@article{SperlichHahnEdeletal.2018, author = {Sperlich, Billy and Hahn, Lea-Sofie and Edel, Antonia and Behr, Tino and Helmprobst, Julian and Leppich, Robert and Wallmann-Sperlich, Birgit and Holmberg, Hans-Christer}, title = {A 4-week intervention involving mobile-based daily 6-minute micro-sessions of functional high-intensity circuit training improves strength and quality of life, but not cardio-respiratory fitness of young untrained adults}, series = {Frontiers in Physiology}, volume = {9}, journal = {Frontiers in Physiology}, number = {423}, doi = {10.3389/fphys.2018.00423}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176565}, year = {2018}, abstract = {The present study was designed to assess the psycho-physiological responses of physically untrained individuals to mobile-based multi-stimulating, circuit-like, multiple-joint conditioning (Circuit\(_{HIIT}\)) performed either once (1xCircuitHIIT) or twice (2xCircuit\(_{HIIT}\)) daily for 4 weeks. In this single-center, two-arm randomized, controlled study, 24 men and women (age: 25 ± 5 years) first received no training instructions for 4 weeks and then performed 4 weeks of either 1xCircuitHIIT or 2xCircuit\(_{HIIT}\) (5 men and 7 women in each group) daily. The 1xCircuitHIIT and 2xCircuit\(_{HIIT}\) participants carried out 90.7 and 85.7\% of all planned training sessions, respectively, with average heart rates during the 6-min sessions of 74.3 and 70.8\% of maximal heart rate. Body, fat and fat-free mass, and metabolic rate at rest did not differ between the groups or between time-points of measurement. Heart rate while running at 6 km⋅h\(^{-1}\) declined after the intervention in both groups. Submaximal and peak oxygen uptake, the respiratory exchange ratio and heart rate recovery were not altered by either intervention. The maximal numbers of push-ups, leg-levers, burpees, 45°-one-legged squats and 30-s skipping, as well as perception of general health improved in both groups. Our 1xCircuit\(_{HIIT}\) or 2xCircuit\(_{HIIT}\) interventions improved certain parameters of functional strength and certain dimensions of quality of life in young untrained individuals. However, they were not sufficient to enhance cardio-respiratory fitness, in particular peak oxygen uptake.}, language = {en} } @article{KraftBirkReichertetal.2020, author = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger}, title = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s20123456}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826}, year = {2020}, abstract = {Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.}, language = {en} } @article{DuekingTaflerWallmannSperlichetal.2020, author = {D{\"u}king, Peter and Tafler, Marie and Wallmann-Sperlich, Birgit and Sperlich, Billy and Kleih, Sonja}, title = {Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis}, series = {JMIR Mhealth and Uhealth}, volume = {8}, journal = {JMIR Mhealth and Uhealth}, number = {11}, doi = {10.2196/20820}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-230556}, year = {2020}, abstract = {Background: Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. Objective: The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users' PA behavior. Methods: The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin V{\´i}voactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. Results: The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin V{\´i}voactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. Conclusions: Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.}, language = {en} } @article{UnnikrishnanSchleicherShahetal.2020, author = {Unnikrishnan, Vishnu and Schleicher, Miro and Shah, Yash and Jamaludeen, Noor and Pryss, Ruediger and Schobel, Johannes and Kraft, Robin and Schlee, Winfried and Spiliopoulou, Myra}, title = {The effect of non-personalised tips on the continued use of self-monitoring mHealth applications}, series = {Brain Sciences}, volume = {10}, journal = {Brain Sciences}, number = {12}, issn = {2076-3425}, doi = {10.3390/brainsci10120924}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219435}, year = {2020}, abstract = {Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients' quality of life. Recent advances in mHealth have enabled patients to maintain a 'disease journal' of ecologically-valid momentary assessments, improving patients' own awareness of their disease while also providing clinicians valuable data for research. In this study, we investigate the effect of non-personalised tips on patients' perception of tinnitus, and on their continued use of the application. The data collected from the study involved three groups of patients that used the app for 16 weeks. Groups A \& Y were exposed to feedback from the start of the study, while group B only received tips for the second half of the study. Groups A and Y were run by different supervisors and also differed in the number of hospital visits during the study. Users of Group A and B underwent assessment at baseline, mid-study, post-study and follow-up, while users of group Y were only assessed at baseline and post-study. It is seen that the users in group B use the app for longer, and also more often during the day. The answers of the users to the Ecological Momentary Assessments are seen to form clusters where the degree to which the tinnitus distress depends on tinnitus loudness varies. Additionally, cluster-level models were able to predict new unseen data with better accuracy than a single global model. This strengthens the argument that the discovered clusters really do reflect underlying patterns in disease expression.}, language = {en} } @article{PryssSchleeHoppenstedtetal.2020, author = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas}, title = {Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study}, series = {Journal of Medical Internet Research}, volume = {22}, journal = {Journal of Medical Internet Research}, number = {6}, doi = {10.2196/15547}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517}, year = {2020}, abstract = {Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1\% to 42.7\% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.}, language = {en} } @article{KraftReichertPryss2021, author = {Kraft, Robin and Reichert, Manfred and Pryss, R{\"u}diger}, title = {Towards the interpretation of sound measurements from smartphones collected with mobile crowdsensing in the healthcare domain: an experiment with Android devices}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {1}, issn = {1424-8220}, doi = {10.3390/s22010170}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252246}, year = {2021}, abstract = {The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.}, language = {en} } @article{HolfelderMulanskySchleeetal.2021, author = {Holfelder, Marc and Mulansky, Lena and Schlee, Winfried and Baumeister, Harald and Schobel, Johannes and Greger, Helmut and Hoff, Andreas and Pryss, R{\"u}diger}, title = {Medical device regulation efforts for mHealth apps during the COVID-19 pandemic — an experience report of Corona Check and Corona Health}, series = {J — Multidisciplinary Scientific Journal}, volume = {4}, journal = {J — Multidisciplinary Scientific Journal}, number = {2}, issn = {2571-8800}, doi = {10.3390/j4020017}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285434}, pages = {206 -- 222}, year = {2021}, abstract = {Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a "medical app". However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team — especially software engineers — are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.}, language = {en} } @article{KerwagenFuchsUllrichetal.2023, author = {Kerwagen, Fabian and Fuchs, Konrad F. and Ullrich, Melanie and Schulze, Andres and Straka, Samantha and Krop, Philipp and Latoschik, Marc E. and Gilbert, Fabian and Kunz, Andreas and Fette, Georg and St{\"o}rk, Stefan and Ertl, Maximilian}, title = {Usability of a mHealth solution using speech recognition for point-of-care diagnostic management}, series = {Journal of Medical Systems}, volume = {47}, journal = {Journal of Medical Systems}, number = {1}, doi = {10.1007/s10916-022-01896-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324002}, year = {2023}, abstract = {The administrative burden for physicians in the hospital can affect the quality of patient care. The Service Center Medical Informatics (SMI) of the University Hospital W{\"u}rzburg developed and implemented the smartphone-based mobile application (MA) ukw.mobile1 that uses speech recognition for the point-of-care ordering of radiological examinations. The aim of this study was to examine the usability of the MA workflow for the point-of-care ordering of radiological examinations. All physicians at the Department of Trauma and Plastic Surgery at the University Hospital W{\"u}rzburg, Germany, were asked to participate in a survey including the short version of the User Experience Questionnaire (UEQ-S) and the Unified Theory of Acceptance and Use of Technology (UTAUT). For the analysis of the different domains of user experience (overall attractiveness, pragmatic quality and hedonic quality), we used a two-sided dependent sample t-test. For the determinants of the acceptance model, we employed regression analysis. Twenty-one of 30 physicians (mean age 34 ± 8 years, 62\% male) completed the questionnaire. Compared to the conventional desktop application (DA) workflow, the new MA workflow showed superior overall attractiveness (mean difference 2.15 ± 1.33), pragmatic quality (mean difference 1.90 ± 1.16), and hedonic quality (mean difference 2.41 ± 1.62; all p < .001). The user acceptance measured by the UTAUT (mean 4.49 ± 0.41; min. 1, max. 5) was also high. Performance expectancy (beta = 0.57, p = .02) and effort expectancy (beta = 0.36, p = .04) were identified as predictors of acceptance, the full predictive model explained 65.4\% of its variance. Point-of-care mHealth solutions using innovative technology such as speech-recognition seem to address the users' needs and to offer higher usability in comparison to conventional technology. Implementation of user-centered mHealth innovations might therefore help to facilitate physicians' daily work.}, language = {en} }