TY - JOUR A1 - Chromik, Jonas A1 - Kirsten, Kristina A1 - Herdick, Arne A1 - Kappattanavar, Arpita Mallikarjuna A1 - Arnrich, Bert T1 - SensorHub BT - Multimodal sensing in real-life enables home-based studies JF - Sensors N2 - Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life. KW - multimodal sensing KW - home-based studies KW - activity recognition KW - sensor KW - systems KW - ecological momentary assessment KW - digital health Y1 - 2022 U6 - https://doi.org/10.3390/s22010408 SN - 1424-8220 VL - 22 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander M. A1 - Strelow, Nils A1 - Ruether, Darius F. A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan A. A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Böttinger, Erwin T1 - StudyU: a platform for designing and conducting innovative digital N-of-1 trials JF - Journal of medical internet research N2 - N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2022 U6 - https://doi.org/10.2196/35884 SN - 1439-4456 SN - 1438-8871 VL - 24 IS - 7 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Lewkowicz, Daniel A1 - Wohlbrandt, Attila M. A1 - Böttinger, Erwin T1 - Digital therapeutic care apps with decision-support interventions for people with low back pain in Germany BT - Cost-effectiveness analysis JF - JMIR mhealth and uhealth N2 - Background: Digital therapeutic care apps provide a new effective and scalable approach for people with nonspecific low back pain (LBP). Digital therapeutic care apps are also driven by personalized decision-support interventions that support the user in self-managing LBP, and may induce prolonged behavior change to reduce the frequency and intensity of pain episodes. However, these therapeutic apps are associated with high attrition rates, and the initial prescription cost is higher than that of face-to-face physiotherapy. In Germany, digital therapeutic care apps are now being reimbursed by statutory health insurance; however, price targets and cost-driving factors for the formation of the reimbursement rate remain unexplored. Objective: The aim of this study was to evaluate the cost-effectiveness of a digital therapeutic care app compared to treatment as usual (TAU) in Germany. We further aimed to explore under which circumstances the reimbursement rate could be modified to consider value-based pricing. Methods: We developed a state-transition Markov model based on a best-practice analysis of prior LBP-related decision-analytic models, and evaluated the cost utility of a digital therapeutic care app compared to TAU in Germany. Based on a 3-year time horizon, we simulated the incremental cost and quality-adjusted life years (QALYs) for people with nonacute LBP from the societal perspective. In the deterministic sensitivity and scenario analyses, we focused on diverging attrition rates and app cost to assess our model's robustness and conditions for changing the reimbursement rate. All costs are reported in Euro (euro1=US $1.12). Results: Our base case results indicated that the digital therapeutic care strategy led to an incremental cost of euro121.59, but also generated 0.0221 additional QALYs compared to the TAU strategy, with an estimated incremental cost-effectiveness ratio (ICER) of euro5486 per QALY. The sensitivity analysis revealed that the reimbursement rate and the capability of digital therapeutic care to prevent reoccurring LBP episodes have a significant impact on the ICER. At the same time, the other parameters remained unaffected and thus supported the robustness of our model. In the scenario analysis, the different model time horizons and attrition rates strongly influenced the economic outcome. Reducing the cost of the app to euro99 per 3 months or decreasing the app's attrition rate resulted in digital therapeutic care being significantly less costly with more generated QALYs, and is thus considered to be the dominant strategy over TAU. Conclusions: The current reimbursement rate for a digital therapeutic care app in the statutory health insurance can be considered a cost-effective measure compared to TAU. The app's attrition rate and effect on the patient's prolonged behavior change essentially influence the settlement of an appropriate reimbursement rate. Future value-based pricing targets should focus on additional outcome parameters besides pain intensity and functional disability by including attrition rates and the app's long-term effect on quality of life. KW - cost-utility analysis KW - low back pain KW - back pain KW - cost-effectiveness KW - Markov model KW - digital therapy KW - digital health app KW - mHealth KW - orthopedic; KW - eHealth KW - mobile health KW - digital health KW - pain management KW - health apps Y1 - 2022 U6 - https://doi.org/10.2196/35042 SN - 2291-5222 VL - 10 IS - 2 PB - JMIR Publications CY - Toronto ER - TY - JOUR A1 - Schachner, Theresa A1 - Gross, Christoph A1 - Hasl, Andrea A1 - Wangenheim, Florian von A1 - Kowatsch, Tobias T1 - Deliberative and paternalistic interaction styles for conversational agents in digital health BT - procedure and validation through a web-based experiment JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users. Objective: The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context. Methods: On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style. Results: A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X-1(,8)8(2)=38.2; P<.001; phi coefficient r(phi)=0.68). The validation of the procedure was hence successful. Conclusions: We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users. KW - conversational agents KW - chatbots KW - human-computer interaction KW - physician-patient relationship KW - interaction styles KW - deliberative KW - interaction KW - paternalistic interaction KW - digital health KW - chronic KW - conditions KW - COPD Y1 - 2021 U6 - https://doi.org/10.2196/22919 SN - 1438-8871 VL - 23 IS - 1 PB - Healthcare World CY - Richmond, Va. ER - TY - GEN A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander Maximilian A1 - Strelow, Nils A1 - Ruether, Darius Ferenc A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan Antonio A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Bottinger, Erwin T1 - StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials T2 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 12 KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-580370 IS - 12 ER - TY - JOUR A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander Maximilian A1 - Strelow, Nils A1 - Ruether, Darius Ferenc Ruether A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan Antonio A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Bottinger, Erwin T1 - StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials JF - Journal of Medical Internet Research N2 - N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2021 U6 - https://doi.org/10.2196/35884 SN - 1438-8871 VL - 24 PB - JMIR Publications CY - Richmond, Virginia, USA ET - 7 ER - TY - GEN A1 - Albert, Justin Amadeus A1 - Owolabi, Victor A1 - Gebel, Arnd A1 - Brahms, Clemens Markus A1 - Granacher, Urs A1 - Arnrich, Bert T1 - Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard BT - A Pilot Study T2 - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 3 KW - motion capture KW - evaluation KW - human motion KW - RGB-D cameras KW - digital health Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-484130 IS - 3 ER - TY - JOUR A1 - Albert, Justin Amadeus A1 - Owolabi, Victor A1 - Gebel, Arnd A1 - Brahms, Clemens Markus A1 - Granacher, Urs A1 - Arnrich, Bert T1 - Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard BT - A Pilot Study JF - Sensors N2 - Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people. KW - motion capture KW - evaluation KW - human motion KW - RGB-D cameras KW - digital health Y1 - 2020 U6 - https://doi.org/10.3390/s20185104 SN - 1424-8220 VL - 20 IS - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Salzwedel, Annett A1 - Rabe, Sophie A1 - Zahn, Thomas A1 - Neuwirth, Julia A1 - Eichler, Sarah A1 - Haubold, Kathrin A1 - Wachholz, Anne A1 - Reibis, Rona Katharina A1 - Völler, Heinz T1 - Nutzerinteresse an mobilen digitalen Assistenzsystemen zur Förderung körperlicher Aktivität bei Studierenden und Mitarbeitern einer Universität T1 - User Interest in Digital Health Technologies to Enhance Physical Activity in Students and Staff of a University JF - Gesundheitswesen N2 - Hintergrund Einem Großteil der Bevölkerung gelingt es trotz ausreichenden Wissens um die protektiven Effekte nicht, ausreichende körperliche Aktivität in den Alltag zu integrieren. Digitale Assistenzsysteme könnten hierbei unterstützend eingesetzt werden. Dies setzt jedoch das Interesse potentieller Nutzer voraus. Methode In einer Online-Befragung wurden im Juni/Juli 2015 Mitarbeiter und Studierende der Universität Potsdam zum individuellen Ausmaß der sportlichen Aktivität, dem Interesse an elektronischer Trainingsunterstützung und weiteren Parametern befragt. Ergebnis 1217 Studierende und 485 Mitarbeiter (67,3 bzw. 67,5% Frauen, 26±4,9 bzw. 42,7±11,7 Jahre) nahmen an der Studie teil. Die empfohlene sportliche Aktivität (≥3 Tage bzw. 150 min/Woche) wurde von 70,1% der Mitarbeiter und 52,7% der Studierenden nicht erreicht. Innerhalb dieser Gruppen zeigten 53,2% (Studierende) bzw. 44,2% (Mitarbeiter), unabhängig von Alter, Geschlecht, BMI bzw. Bildungsniveau, Interesse an einer elektronischen Trainingsunterstützung. Schlussfolgerung Auch in jüngeren Bevölkerungsgruppen mit hohem Bildungsniveau ist die Mehrzahl der Personen unzureichend körperlich aktiv. Ein Interesse an Trainingsunterstützung besteht in etwa der Hälfte dieser sportlich inaktiven Gruppe. Dies legt den Schluss nahe, dass der personalisierte Einsatz mobiler Assistenzsysteme für die positive Beeinflussung des Lebensstils zunehmend an Bedeutung gewinnen könnte. N2 - Introduction Though health-enhancing effects of physical activity are well documented, the majority of the population is unable to implement present recommendations into daily routine. Mobile health (mHealth) technologies might be able to increase the physical activity level. However, the interest of potential users is a mandatory basis for this. Method We conducted an online-survey from 06-07/2015 by asking students and employees from the University of Potsdam for their activity level, interest in mHealth training support and other relevant parameters. Results 1 217 students and 485 employees (67.3 % and 67.5 % female, 26.0 +/- 4.9 and 42.7 +/- 11.7 years, respectively) participated in the survey. 70.1 % of employees and 52.7 % of students did not follow the recommendation for physical activity (3 times per week). 53.2 % (students) and 44.2 % (employees), independent of age, sex, BMI and level of education or professional qualification, indicated their interest in mHealth technology offering training support. Conclusion Even in a younger population with higher education, most respondents reported an insufficient level of physical activity. About half of them indicated their interest in training support. Therefore, the use of personalized mHealth technology may be of increasing significance for a positive change of lifestyle. KW - physical activity KW - digital health KW - prevention KW - lifestyle KW - mHealth KW - körperliche Aktivität KW - digitale Gesundheit KW - Prävention KW - Lebensstil KW - mobile Assistenzsysteme Y1 - 2018 U6 - https://doi.org/10.1055/s-0043-103951 SN - 0941-3790 SN - 1439-4421 VL - 80 IS - 11 SP - 1023 EP - 1025 PB - Thieme CY - Stuttgart ER -