Abstract
Recently, new paradigms like crowdsensing emerged in the context of mobile technologies that promise to support researchers in life sciences and the healthcare domain in a new way. For example, by the use of smartphones, valuable data can be quickly gathered in everyday life and then easily compared to other crowd users, especially when taking environmental factors or sensor data additionally into account. In the context of chronic diseases, mobile technology can particularly help to empower patients in coping with their individual health situation more properly. However, to utilize the achievements of mobile technology in the aforementioned contexts is still challenging. Following this, the work at hand discusses two important and relevant aspects for mobile crowdsensing in healthcare scenarios. First, the status quo of mobile crowdsensing technologies and their relevant perspectives on healthcare scenarios are discussed. Second, salient aspects are presented, which can help researchers to conceptualize mobile crowdsensing to a more generic software toolbox that is able to utilize data gathered with smartphones and their built-in sensors in everyday life. The overall toolbox goal is the support of researchers to conduct studies or analyzes on this new and less understood kind of data source. On top of this, patients shall be empowered to demystify their individual health condition more properly when using the toolbox, especially by exploiting the wisdom of the crowd.
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References
Agrawal K, Mehdi M, Reichert M et al (2018) Towards incentive management mechanisms in the context of crowdsensing technologies based on TrackYourTinnitus insights. In: The 15th international conference on mobile systems and pervasive computing, Gran Canaria, Spain, 13–15 August 2018. Procedia Computer Science, Elsevier Science, pp 145–152
Christin D, Reinhardt A, Kanhere SS, Hollick M (2011) A survey on privacy in mobile participatory sensing applications. J Syst Softw 84(11):1928–1946. https://doi.org/10.1016/j.jss.2011.06.073
Demirbas M, Ali Bayir M, Akcora CG et al (2010) Crowd-sourced sensing and collaboration using twitter. In: 2010 IEEE international symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Montreal, QC, Canada, 14–17 June 2010. IEEE, pp 1–9
Ebner-Priemer UW, Kubiak T (2007) Psychological and psychophysiological ambulatory monitoring. Eur J Psychol Assess 23(4):214–226. https://doi.org/10.1027/1015-5759.23.4.214
Ganti R, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39. https://doi.org/10.1109/MCOM.2011.6069707
Karaliopoulos M, Telelis O, Koutsopoulos I (2015) User recruitment for mobile crowdsensing over opportunistic networks. In: 2015 IEEE conference on computer communications (INFOCOM), Kowloon, Hong Kong, 26 April–1 May 2015. IEEE, pp 2254–2262
Kraft R, Birk F, Reichert M et al (2019) Design and implementation of a scalable crowdsensing platform for geospatial data of tinnitus patients. In: 32nd IEEE CBMS international symposium on computer-based medical systems (CBMS 2019), Cordoba, Spanien, 5–7 June 2019. IEEE
Kubiak T, Smyth JM (2019) Connecting domains—ecological momentary assessment in a mobile sensing framework. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Luo T, Kanhere SS, Huang J et al (2017) Sustainable incentives for mobile crowdsensing: auctions, lotteries, and trust and reputation systems. IEEE Commun Mag 55(3):68–74. https://doi.org/10.1109/MCOM.2017.1600746CM
Ma H, Zhao D, Yuan P (2014) Opportunities in mobile crowd sensing. IEEE Commun Mag 52(8):29–35. https://doi.org/10.1109/MCOM.2014.6871666
Messner E-M, Probst T, O’Rourke T et al (2019) mHealth applications: potentials, limitations, current quality and future directions. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Montag C, Baumeister H, Kannen C et al (2019) Concept, possibilities and pilot-testing of a new smartphone application for the social and life sciences to study human behavior including validation data from personality psychology. J 2(2):102–115. https://doi.org/10.3390/j2020008
Myin-Germeys I, Oorschot M, Collip D et al (2009) Experience sampling research in psychopathology: opening the black box of daily life. Psychol Med 39(9):1533–1547. https://doi.org/10.1017/S0033291708004947
Probst T, Pryss R, Langguth B, Schlee W (2016) Emotional states as mediators between tinnitus loudness and tinnitus distress in daily life: Results from the “TrackYourTinnitus” application. Sci Rep 6(1):20382. https://doi.org/10.1038/srep20382
Probst T, Pryss RC, Langguth B et al (2017) Does tinnitus depend on time-of-day? An ecological momentary assessment study with the “TrackYourTinnitus” application. Front Aging Neurosci 9:253. https://doi.org/10.3389/fnagi.2017.00253
Pryss R, Reichert M, Langguth B, Schlee W (2015) Mobile crowd sensing services for tinnitus assessment, therapy, and research. In: 2015 IEEE international conference on mobile services, New York City, NY, USA, 27 June–2 July 2015. IEEE, pp 352–359
Pryss R, Probst T, Schlee W et al (2017a) Mobile crowdsensing for the juxtaposition of realtime assessments and retrospective reporting for neuropsychiatric symptoms. In: 2017 IEEE 30th international symposium on computer-based medical systems (CBMS), Thessaloniki, Greece, 22–24 June 2017. IEEE, pp 642–647
Pryss R, Schlee W, Langguth B, Reichert M (2017b) Mobile crowdsensing services for tinnitus assessment and patient feedback. In: 2017 IEEE international conference on AI & mobile services (AIMS), Honolulu, HI, USA, 25–30 June 2017. IEEE, pp 22–29
Pryss R, Probst T, Schlee W et al (2018a) Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform. Int J Data Sci Anal: 1–12. https://doi.org/10.1007/s41060-018-0111-4
Pryss R, Schobel J, Reichert M (2018b) Requirements for a flexible and generic API enabling mobile crowdsensing mHealth applications. In: 2018 4th international workshop on requirements engineering for self-adaptive, collaborative, and cyber physical systems (RESACS), Banff, AB, Canada, 20 August 2018. IEEE, pp 24–31
Pryss R, Kraft R, Baumeister H et al (2019) Using Chatbots to support medical and psychological treatment procedures. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Rozgonjuk D, Elhai JD, Hall BJ (2019) Studying psychopathology in relation to smartphone use. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Ruf-Leuschner M, Brunnemann N, Schauer M et al (2016) The KINDEX-App—an instrument for assessment and immediate analysis of psychosocial risk factors in pregnant women in daily practice by gynecologists, midwives and in gynecological hospitals. Verhaltenstherapie 26(3):171–181. https://doi.org/10.1159/000448455
Sariyska R, Montag C (2019) Smartphone supported psychodiagnostics in the assessment of personality and physical activity. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Sariyska R, Rathner E-M, Baumeister H, Montag C (2018) Feasibility of linking molecular genetic markers to real-world social network size tracked on smartphones. Front Neurosci 12:945. https://doi.org/10.3389/fnins.2018.00945
Schickler M, Reichert M, Pryss R et al (2015) Entwicklung mobiler Apps: Konzepte, Anwendungsbausteine und Werkzeuge im Business und E-Health. Springer, Berlin, Heidelber
Schlee W, Kraft R, Schobel J et al (2019) Momentary assessment of tinnitus—how smart mobile applications advance our understanding of tinnitus. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Schobel J, Pryss R, Schlee W et al (2017) Development of mobile data collection applications by domain experts: experimental results from a usability study. In: Dubois E, Pohl K (eds) Advanced information systems engineering, CAiSE 2017. Lecture notes in computer science. Springer International Publishing, Cham, pp 60–75
Shu L, Chen Y, Huo Z et al (2017) When mobile crowd sensing meets traditional industry. IEEE Access 5:15300–15307. https://doi.org/10.1109/ACCESS.2017.2657820
Trull TJ, Ebner-Priemer U (2013) Ambulatory assessment. Annu Rev Clin Psychol 9(1):151–176. https://doi.org/10.1146/annurev-clinpsy-050212-185510
Vaid SS, Harari GM (2019) Smartphones in personal informatics: Self-tracking with mobile sensing for behavior change. In: Montag C, Baumeister H (eds) Mobile sensing and digital phenotyping: new developments in psychoinformatics. Springer, Berlin, pp xx–xx
Wan J, Liu J, Shao Z et al (2016) Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors 16(1):88. https://doi.org/10.3390/s16010088
Xiong H, Huang Y, Barnes LE, Gerber MS (2016) Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing—UbiComp ’16, Heidelberg, Germany, 12–16 September 2016. ACM Press, pp 415–426
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Pryss, R. (2019). Mobile Crowdsensing in Healthcare Scenarios: Taxonomy, Conceptual Pillars, Smart Mobile Crowdsensing Services. In: Baumeister, H., Montag, C. (eds) Digital Phenotyping and Mobile Sensing. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-31620-4_14
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