Evangelische Hochschule Nürnberg
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A longitudinal pilot study on stress-levels in the crowdsensing mHealth platform TrackYourStress
(2019)
Background: The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels.
Objective: In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS.
Methods: Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity.
Results: Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04).
Conclusions: TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.
Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises)to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical databased on the power of the crowd and the offered computationalcapabilities of mobile devices. Notably, collecting data withmobile crowdsensing solutions has several advantages comparedto traditional assessment methods when gathering data overtime. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goalto predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.
Editorial
(2019)
Das Projekt „Gesunde Südstadt“ hat den Schwerpunkt Gesundheitsförderung in den Lebenswelten. Es wurde im Rahmen des Programms „Gesunde Kommune“ der AOK Bayern initiiert und zielt auf einen Beitrag zur Verringerung der gesundheitlichen Ungleichheit im sozioökonomisch benachteiligten südlichen Bezirk der Stadt Nürnberg ab. Es werden bedarfsgerechte Präventions- und Gesundheitsförderungsmaßnahmen mit sozial benachteiligten Zielgruppen (Migranten, Geflüchtete, Erwerbslose) entwickelt und umgesetzt. Die Maßnahmen des Projekts orientieren sich an den Handlungsfeldern Ernährung, Bewegung und Stressmanagement des Leitfadens Prävention. Die Wirksamkeit des Projekts wird durch eine externe multiperspektivische, multimethodische Prozess- und Ergebnisevaluation untersucht.
Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users.
Inkompatibilität
(2019)
Bayernpartei (BP)
(2019)
Stem cell research has been a problematic endeavour. For the past twenty years it has attracted moral controversies in both the public and the professional sphere. The research involves not only laboratories, clinics and people, but ethics, industries, jurisprudence, and markets. Today it contributes to the development of new therapies and affects increasingly many social arenas. The matrix approach introduced in this book offers a new understanding of this science in its relation to society. The contributions are multidisciplinary and intersectional, illustrating how agency and influence between science and society go both ways.
Conceptually, this volume presents a situated and reflexive approach for philosophy and sociology of the life sciences. The practices that are part of stem cell research are dispersed, and the concepts that tie them together are tenuous; there are persistent problems with the validation of findings, and the ontology of the stem cell is elusive. The array of applications shapes a growing bioeconomy that is dependent on patient donations of tissues and embryos, consumers, and industrial support. In this volume it is argued that this research now denotes not a specific field but a flexible web of intersecting practices, discourses, and agencies. To capture significant parts of this complex reality, this book presents recent findings from researchers, who have studied in-depth aspects of this matrix of stem cell research.
This volume presents state-of-the-art examinations from senior and junior scholars in disciplines from humanities and laboratory research to various social sciences, highlighting particular normative and epistemological intersections. The book will appeal to scholars as well as wider audiences interested in developments in life science and society interactions. The novel matrix approach and the accessible case studies make this an excellent resource for science and society courses.