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Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications: A Systematic Review

Published:30 December 2020Publication History
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Abstract

With the widespread use of smartphones and wearable health sensors, a plethora of mobile health (mHealth) applications to track well-being, run human behavioral studies, and clinical trials have emerged in recent years. However, the design, development, and deployment of mHealth applications is challenging in many ways. To address these challenges, several generic mobile sensing frameworks have been researched in the past decade. Such frameworks assist developers and researchers in reducing the complexity, time, and cost required to build and deploy health-sensing applications. The main goal of this article is to provide the reader with an overview of the state-of-the-art of health-focused generic mobile and wearable sensing frameworks. This review gives a detailed analysis of functional and non-functional features of existing frameworks, the health studies they were used in, and the stakeholders they support. Additionally, we also analyze the historical evolution, uptake, and maintenance after the initial release. Based on this analysis, we suggest new features and opportunities for future generic mHealth sensing frameworks.

References

  1. Kevin MacG Adams et al. 2015. Nonfunctional Requirements in Systems Analysis and Design. Vol. 28. Springer.Google ScholarGoogle Scholar
  2. Sasan Adibi. 2015. Mobile Health: A Technology Road Map. Vol. 5. Springer.Google ScholarGoogle Scholar
  3. Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, and Alex Pentland. 2011. Social fMRI: Investigating and shaping social mechanisms in the real world. Pervas. Mob. Comput. 7, 6 (2011), 643--659.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Shahriar Akter and Pradeep Ray. 2010. mHealth—An ultimate platform to serve the unserved. Yearb. Med. Inf. 19, 01 (2010), 94--100.Google ScholarGoogle Scholar
  5. Talayeh Aledavood, Ana Maria Triana Hoyos, Tuomas Alakörkkö, Kimmo Kaski, Jari Saramäki, Erkki Isometsä, and Richard K. Darst. 2017. Data collection for mental health studies through digital platforms: Requirements and design of a prototype. JMIR Res. Protoc. 6, 6 (2017), e110.Google ScholarGoogle ScholarCross RefCross Ref
  6. Apple. 2016. CareKit. Retrieved March 2, 2019 from http://carekit.org.Google ScholarGoogle Scholar
  7. Joost Asselbergs, Jeroen Ruwaard, Michal Ejdys, Niels Schrader, Marit Sijbrandij, and Heleen Riper. 2016. Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: An explorative study. J. Med. Internet Res. 18, 3 (2016), e72.Google ScholarGoogle ScholarCross RefCross Ref
  8. Sangwon Bae, Tammy Chung, Denzil Ferreira, Anind K. Dey, and Brian Suffoletto. 2018. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addict. Behav. 83 (2018), 42--47.Google ScholarGoogle ScholarCross RefCross Ref
  9. Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting drinking episodes in young adults using smartphone-based sensors. Proc. ACM Interact. Mob. Wearable Ubiq. Technol. 1, 2 (June 2017). DOI:https://doi.org/10.1145/3090051Google ScholarGoogle Scholar
  10. Jakob E. Bardram and Mads Frost. 2016. The personal health technology design space. IEEE Pervas. Comput. 15, 2 (2016), 70--78.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Sage Bionetworks. (2018). Bridge Sensing Platform. Retrieved March 2, 2019 from https://developer.sagebridge.org/articles/overview.html.Google ScholarGoogle Scholar
  12. Brian M. Bot, Christine Suver, Elias Chaibub Neto, Michael Kellen, Arno Klein, Christopher Bare, Megan Doerr, Abhishek Pratap, John Wilbanks, E. Ray Dorsey, et al. 2016. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci. Data 3 (2016), 160011.Google ScholarGoogle Scholar
  13. Waylon Brunette, Rita Sodt, Rohit Chaudhri, Mayank Goel, Michael Falcone, Jaylen Van Orden, and Gaetano Borriello. 2012. Open data kit sensors: A sensor integration framework for Android at the application-level. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM, 351--364.Google ScholarGoogle Scholar
  14. Lora E. Burke, Jun Ma, Kristen M. J. Azar, Gary G. Bennett, Eric D. Peterson, Yaguang Zheng, William Riley, Janna Stephens, Svati H. Shah, Brian Suffoletto, et al. 2015. Current science on consumer use of mobile health for cardiovascular disease prevention: A scientific statement from the American Heart Association. Circulation 132, 12 (2015), 1157--1213.Google ScholarGoogle ScholarCross RefCross Ref
  15. Rohit Chaudhri, Waylon Brunette, Bruce Hemingway, and Gaetano Borriello. 2013. ODK sensors: An application-level sensor framework for Android devices. In Proceedings of the 3rd ACM Symposium on Computing for Development. ACM, 30.Google ScholarGoogle Scholar
  16. Delphine Christin, Andreas Reinhardt, Salil S. Kanhere, and Matthias Hollick. 2011. A survey on privacy in mobile participatory sensing applications. J. Syst. Softw. 84, 11 (2011), 1928--1946.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Chia-Fang Chung, Jonathan Cook, Elizabeth Bales, Jasmine Zia, and Sean A. Munson. 2015. More than telemonitoring: Health provider use and nonuse of life-log data in irritable bowel syndrome and weight management. J. Med. Internet Res. 17, 8 (2015), e203.Google ScholarGoogle ScholarCross RefCross Ref
  18. European Commission. 2019. Data protection. Retrieved March 2, 2019 from https://ec.europa.eu/info/law/law-topic/data-protection_en.Google ScholarGoogle Scholar
  19. IEEE Standards Coordinating Committee et al. 1990. IEEE standard glossary of software engineering terminology (IEEE Std 610.12-1990).. IEEE Computer Society Los Alamitos, CA.Google ScholarGoogle Scholar
  20. Confluent. 2019. Kafka Connect | Confluent. Retrieved February 28, 2019 from https://www.confluent.io/product/connectors/.Google ScholarGoogle Scholar
  21. Mary Jo Deering, Erin Siminerio, and Scott Weinstein. 2013. Issue Brief: Patient-generated Health Data and Health IT. Office of the National Coordinator for Health Information Technology.Google ScholarGoogle Scholar
  22. David Daniel Ebert, Pim Cuijpers, Ricardo F. Muñoz, and Harald Baumeister. 2017. Prevention of mental health disorders using internet-and mobile-based interventions: A narrative review and recommendations for future research. Front. Psychiat. 8 (2017), 116.Google ScholarGoogle Scholar
  23. Certification Europe. 2019. BS 10012 Personal Information Management System. Retrieved March 2, 2019 from https://www.certificationeurope.com/certification/bs-10012-personal-information-management-systems/.Google ScholarGoogle Scholar
  24. Denzil Ferreira, Vassilis Kostakos, and Anind K. Dey. 2015. AWARE: Mobile context instrumentation framework. Front. ICT 2 (2015), 6.Google ScholarGoogle ScholarCross RefCross Ref
  25. Fitbit. 2015. Fitbit. Retrieved March 5, 2019 from https://www.fitbit.com.Google ScholarGoogle Scholar
  26. Funf. 2019. Funf. Retrieved March 2, 2019 from http://www.funf.org/about.html.Google ScholarGoogle Scholar
  27. Andrea Gaggioli, Giovanni Pioggia, Gennaro Tartarisco, Giovanni Baldus, Daniele Corda, Pietro Cipresso, and Giuseppe Riva. 2013. A mobile data collection platform for mental health research. Pers. Ubiq. Comput. 17, 2 (Feb. 2013), 241--251. DOI:https://doi.org/10.1007/s00779-011-0465-2Google ScholarGoogle Scholar
  28. GoogleFit. 2014. Google Fit. Retrieved April 2, 2019 from https://www.google.com/fit/.Google ScholarGoogle Scholar
  29. Mohammad Hashemian, Dylan Knowles, Jonathan Calver, Weicheng Qian, Michael C. Bullock, Scott Bell, Regan L. Mandryk, Nathaniel Osgood, and Kevin G. Stanley. 2012. iEpi: An end to end solution for collecting, conditioning and utilizing epidemiologically relevant data. In Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare. ACM, 3--8.Google ScholarGoogle Scholar
  30. HealthKit. 2014. HealthKit. Retrieved April 2, 2019 from https://developer.apple.com/healthkit/.Google ScholarGoogle Scholar
  31. Netzahualcóyotl Hernández, Bert Arnrich, Jesús Favela, Cem Ersoy, Burcu Demiray, and Jesús Fontecha. 2019. A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform. J. Amb. Intell. Hum. Comput. 10, 6 (2019), 2199--2211.Google ScholarGoogle ScholarCross RefCross Ref
  32. John Hicks, Nithya Ramanathan, Donnie Kim, Mohamad Monibi, Joshua Selsky, Mark Hansen, and Deborah Estrin. 2010. AndWellness: An open mobile system for activity and experience sampling. In Proceedings of the Wireless Health Conference. ACM, 34--43.Google ScholarGoogle Scholar
  33. Mark D. Hill. 1990. What is scalability?ACM SIGARCH Comput. Archit. News 18, 4 (1990), 18--21.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Health Level 7 (HL7). 2016. HL7 Mobile Health. Retrieved December 2, 2019 from http://www.hl7.org/Special/committees/mobile/index.cfm.Google ScholarGoogle Scholar
  35. Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A mobile sensing software platform for development and validation of digital biomarkers and interventions. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM, 7.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Karen Hovsepian, Mustafa al’Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: Towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 493--504.Google ScholarGoogle Scholar
  37. Cheng-Kang Hsieh, Hongsuda Tangmunarunkit, Faisal Alquaddoomi, John Jenkins, Jinha Kang, Cameron Ketcham, Brent Longstaff, Joshua Selsky, Betta Dawson, Dallas Swendeman, et al. 2013. Lifestreams: A modular sense-making toolset for identifying important patterns from everyday life. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. ACM, 5.Google ScholarGoogle Scholar
  38. Intel. 2015. Intel Context Sensing SDK. Retrieved March 12, 2019 from https://software.intel.com/en-us/context-sensing-sdk/features.Google ScholarGoogle Scholar
  39. Kleomenis Katevas, Hamed Haddadi, and Laurissa Tokarchuk. 2014. Poster: Sensingkit: A multi-platform mobile sensing framework for large-scale experiments. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. ACM, 375--378.Google ScholarGoogle Scholar
  40. Alexander D. Kent and Lorie M. Liebrock. 2011. Secure communication via shared knowledge and a salted hash in ad-hoc environments. In Proceedings of the IEEE 35th Annual Computer Software and Applications Conference Workshops. IEEE, 122--127.Google ScholarGoogle Scholar
  41. Jayden Khakurel, Helinä Melkas, and Jari Porras. 2018. Tapping into the wearable device revolution in the work environment: A systematic review. Inf. Technol. People 31, 3 (2018), 791--818.Google ScholarGoogle Scholar
  42. Wazir Zada Khan, Yang Xiang, Mohammed Y. Aalsalem, and Quratulain Arshad. 2012. Mobile phone sensing systems: A survey. IEEE Commun. Surv. Tutor. 15, 1 (2012), 402--427.Google ScholarGoogle ScholarCross RefCross Ref
  43. Barbara Kitchenham and Stuart Charters. 2007. Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01. Software Engineering Group, School of Computer Science and Mathematics, Keele University and Department of Computer Science, University of Durham.Google ScholarGoogle Scholar
  44. Dylan L. Knowles, Kevin G. Stanley, and Nathaniel D. Osgood. 2014. A field-validated architecture for the collection of health-relevant behavioural data. In Proceedings of the IEEE International Conference on Healthcare Informatics. IEEE, 79--88.Google ScholarGoogle Scholar
  45. Youngki Lee, S. S. Iyengar, Chulhong Min, Younghyun Ju, Taiwoo Park, Jinwon Lee, Yunseok Rhee, and Junehwa Song. 2012. Mobicon: A mobile context-monitoring platform. Commun. ACM 55, 3 (2012), 54.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Young-Hee Lee and Ryang-Hee Kim. 2018. Estimation of the smartphone user—Satisfaction and customer intention on the social networking service. In Proceedings of the International Conference on Applied Human Factors and Ergonomics. Springer, 262--271.Google ScholarGoogle Scholar
  47. Xinyi Li, Elizabeth Vera, Mark Gilbert, Orieta Celiku, and Terri Armstrong. 2018. INNV-41. My STORI—a symptom tracking and reporting instrument mobile application for central nervous system cancer patients.Neuro-Oncol. 20 (11 2018), vi146--vi146. DOI:https://doi.org/10.1093/neuonc/noy148.609Google ScholarGoogle Scholar
  48. Jong Hyun Lim, Andong Zhan, Evan Goldschmidt, JeongGil Ko, Marcus Chang, and Andreas Terzis. 2012. HealthOS: A platform for pervasive health applications. In Proceedings of the 2nd ACM Workshop on Mobile Systems, Applications, and Services for HealthCare. ACM, 4.Google ScholarGoogle Scholar
  49. Felix Xiaozhu Lin, Ahmad Rahmati, and Lin Zhong. 2010. Dandelion: A framework for transparently programming phone-centered wireless body sensor applications for health. In Proceedings of the Wireless Health Conference (WH’10). ACM, New York, NY, 74--83. DOI:https://doi.org/10.1145/1921081.1921091Google ScholarGoogle Scholar
  50. Carissa A. Low, Anind K. Dey, Denzil Ferreira, Thomas Kamarck, Weijing Sun, Sangwon Bae, and Afsaneh Doryab. 2017. Estimation of symptom severity during chemotherapy from passively sensed data: Exploratory study. J. Med. Internet Res. 19, 12 (19 Dec. 2017), e420. DOI:https://doi.org/10.2196/jmir.9046Google ScholarGoogle Scholar
  51. Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, and Andrew T. Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. ACM, 71--84.Google ScholarGoogle Scholar
  52. Rischan Mafrur, I. Gde Dharma Nugraha, and Deokjai Choi. 2015. Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose. Hum.-cent. Comput. Inf. Sci. 5, 1 (12 Oct. 2015), 31. DOI:https://doi.org/10.1186/s13673-015-0049-7Google ScholarGoogle Scholar
  53. Alex Mariakakis, Edward Wang, Shwetak Patel, and Mayank Goel. 2019. Challenges in realizing smartphone-based health sensing. IEEE Pervas. Comput. 18, 2 (2019), 76--84.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Wendy Berry Mendes. 2018. My BP Lab. Retrieved March 2, 2019 from http://sagebionetworks.org/research-projects/my-bp-lab/.Google ScholarGoogle Scholar
  55. Open mHealth. 2015. Case study: Post-Traumatic Stress (PTSD). Retrieved March 2, 2019 from http://www.openmhealth.org/features/case-studies/case-study-post-traumatic-stress-ptsd/.Google ScholarGoogle Scholar
  56. Open mHealth. 2015. Case study: Type 1 diabetes. Retrieved March 2, 2019 from http://www.openmhealth.org/features/case-studies/case-study-type-1-diabetes/.Google ScholarGoogle Scholar
  57. Open mHealth. (2019). Open mHealth. Retrieved March 5, 2019 from http://www.openmhealth.org/.Google ScholarGoogle Scholar
  58. Chulhong Min, Chungkuk Yoo, Youngki Lee, and Junehwa Song. 2011. Healthopia: Towards your well-being in everyday life. In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies. ACM, 108.Google ScholarGoogle Scholar
  59. Xiaoyun Mo, Dianxi Shi, Ruosong Yang, Han Li, ZheHang Tong, and Feng Wang. 2015. A framework of fine-grained mobile sensing data collection and behavior analysis in an energy-configurable way. In Proceedings of the IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity’15). IEEE, 391--398.Google ScholarGoogle Scholar
  60. W. Morrison, L. Guerdan, J. Kanugo, T. Trull, and Y. Shang. 2018. TigerAware: An innovative mobile survey and sensor data collection and analytics system. In Proceedings of the IEEE 3rd International Conference on Data Science in Cyberspace (DSC’18). 115--122. DOI:https://doi.org/10.1109/DSC.2018.00025Google ScholarGoogle Scholar
  61. Institute of Electrical and Electronics Engineers. 2019. IEEE 11073 Personal health devices. Retrieved December 2, 2019 from https://standards.ieee.org/standard/11073-00103-2012.html.Google ScholarGoogle Scholar
  62. Institute of Electrical and Electronics Engineers. 2019. Standard for Mobile Health Data. Retrieved December 2, 2019 from https://standards.ieee.org/project/1752.html.Google ScholarGoogle Scholar
  63. National Institute of Standards and Technology. 2001. Advanced Encryption Standard (AES). Retrieved March 2, 2019 from https://www.nist.gov/publications/advanced-encryption-standard-aes.Google ScholarGoogle Scholar
  64. Charith Perera, Prem Prakash Jayaraman, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2014. MOSDEN: An internet of things middleware for resource constrained mobile devices. In Proceedings of the 47th Hawaii International Conference on System Sciences. IEEE, 1053--1062.Google ScholarGoogle Scholar
  65. Pascal B. Pfiffner, Isaac Pinyol, Marc D. Natter, and Kenneth D. Mandl. 2016. C3-PRO: Connecting ResearchKit to the health system using i2b2 and FHIR. PloS One 11, 3 (2016), e0152722.Google ScholarGoogle Scholar
  66. Kiran K. Rachuri, Mirco Musolesi, Cecilia Mascolo, Peter J. Rentfrow, Chris Longworth, and Andrius Aucinas. 2010. EmotionSense: A mobile phones based adaptive platform for experimental social psychology research. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing. ACM, 281--290.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Satish Ramkumar, Nitesh Nerlekar, Daniel D’Souza, Derek J. Pol, Jonathan M. Kalman, and Thomas H. Marwick. 2018. Atrial fibrillation detection using single lead portable electrocardiographic monitoring: A systematic review and meta-analysis. BMJ Open 8, 9 (2018), e024178.Google ScholarGoogle Scholar
  68. Yatharth Ranjan, Maximilian Kerz, Zulqarnain Rashid, Sebastian Böttcher, Richard J. B. Dobson, and Amos A. Folarin. 2018. RADAR-base: A novel open source m-health platform. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 223--226.Google ScholarGoogle Scholar
  69. Reza Rawassizadeh, Martin Tomitsch, Katarzyna Wac, and A. Min Tjoa. 2013. UbiqLog: A generic mobile phone-based life-log framework. Person. Ubiq. Comput. 17, 4 (2013), 621--637.Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. ResearchKit. 2015. ResearchKit. Retrieved March 2, 2019 from http://researchkit.org/.Google ScholarGoogle Scholar
  71. ResearchStack. 2016. ResearchStack. Retrieved March 5, 2019 from http://researchstack.org/.Google ScholarGoogle Scholar
  72. Purple Robot. 2015. Purple Robot. Retrieved March 2, 2019 from https://tech.cbits.northwestern.edu/purple-robot/.Google ScholarGoogle Scholar
  73. Darius A. Rohani, Nanna Tuxen, Lars V. Kessing, and Jakob E. Bardram. 2017. Designing for hourly activity sampling in behavioral activation. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth’17). ACM, New York, NY, 431--435. DOI:https://doi.org/10.1145/3154862.3154919.Google ScholarGoogle Scholar
  74. Ángel Ruiz-Zafra, Kawtar Benghazi, Manuel Noguera, and José Luis Garrido. 2013. Zappa: An open mobile platform to build cloud-based m-health systems. In Ambient Intelligence-software and Applications. Springer, 87--94.Google ScholarGoogle Scholar
  75. Sohrab Saeb, Mi Zhang, Christopher J. Karr, Stephen M. Schueller, Marya E. Corden, Konrad P. Kording, and David C. Mohr. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. J. Med. Internet Res. 17, 7 (2015), e175.Google ScholarGoogle ScholarCross RefCross Ref
  76. Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa Al’Absi, and Santosh Kumar. 2015. puffMarker: A multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 999--1010.Google ScholarGoogle Scholar
  77. Akio Sashima, Yutaka Inoue, Takeshi Ikeda, Tomohisa Yamashita, and Koichi Kurumatani. 2008. CONSORTS-S: A mobile sensing platform for context-aware services. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing. IEEE, 417--422.Google ScholarGoogle Scholar
  78. Akio Sashima, Yutaka Inoue, Takeshi Ikeda, Tomohisa Yamashita, Masayuki Ohta, and Koichi Kurumatani. 2008. Toward mobile healthcare services by using everyday mobile phones. In Proceedings of the 1st International Conference on Health Informatics. 242--245.Google ScholarGoogle Scholar
  79. M. Schickler, R. Pryss, M. Stach, J. Schobel, W. Schlee, T. Probst, B. Langguth, and M. Reichert. 2017. An IT platform enabling remote therapeutic interventions. In Proceedings of the IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS’17). 111--116. DOI:https://doi.org/10.1109/CBMS.2017.78.Google ScholarGoogle Scholar
  80. Johannes Schobel, Rüdiger Pryss, Marc Schickler, Martina Ruf-Leuschner, Thomas Elbert, and Manfred Reichert. 2016. End-user programming of mobile services: Empowering domain experts to implement mobile data collection applications. In Proceedings of the IEEE International Conference on Mobile Services (MS’16). IEEE, 1--8.Google ScholarGoogle Scholar
  81. Johannes Schobel, Rüdiger Pryss, Wolfgang Wipp, Marc Schickler, and Manfred Reichert. 2016. A mobile service engine enabling complex data collection applications. In Proceedings of the International Conference on Service-oriented Computing. Springer, 626--633.Google ScholarGoogle Scholar
  82. Audacious Software. 2016. Passive Data Kit. Retrieved March 2, 2019 from https://audacious-software.com/passive-data-kit/.Google ScholarGoogle Scholar
  83. Callum L. Stewart, Zulqarnain Rashid, Yatharth Ranjan, Shaoxiong Sun, Richard J. B. Dobson, and Amos A. Folarin. 2018. RADAR-base: Major depressive disorder and epilepsy case studies. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1735--1743.Google ScholarGoogle Scholar
  84. Hongsuda Tangmunarunkit, Cheng-Kang Hsieh, Brent Longstaff, S. Nolen, John Jenkins, Cameron Ketcham, Joshua Selsky, Faisal Alquaddoomi, Dony George, Jinha Kang, et al. 2015. Ohmage: A general and extensible end-to-end participatory sensing platform. ACM Trans. Intell. Syst. Technol. 6, 3 (2015), 38.Google ScholarGoogle Scholar
  85. John Torous, Mathew V. Kiang, Jeanette Lorme, and Jukka-Pekka Onnela. 2016. New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Health 3, 2 (2016).Google ScholarGoogle Scholar
  86. Jiangtao Wang, Yasha Wang, Daqing Zhang, and Sumi Helal. 2018. Energy saving techniques in mobile crowd sensing: Current state and future opportunities. IEEE Commun. Mag. 56, 5 (2018), 164--169.Google ScholarGoogle ScholarCross RefCross Ref
  87. Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. 2014. StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 3--14.Google ScholarGoogle Scholar
  88. Dan E. Webster, Christine Suver, Megan Doerr, Erin Mounts, Lisa Domenico, Tracy Petrie, Sancy A. Leachman, Andrew D. Trister, and Brian M. Bot. 2017. The mole mapper study, mobile phone skin imaging and melanoma risk data collected using ResearchKit. Sci. Data 4 (2017), 170005.Google ScholarGoogle Scholar
  89. Peter West, Max Van Kleek, Richard Giordano, Mark J. Weal, and Nigel Shadbolt. 2018. Common barriers to the use of patient-generated data across clinical settings. In Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 484.Google ScholarGoogle Scholar
  90. Claes Wohlin. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. ACM, 38.Google ScholarGoogle ScholarDigital LibraryDigital Library
  91. Pang Wu, Huan-Kai Peng, Jiang Zhu, and Ying Zhang. 2011. Senscare: Semi-automatic activity summarization system for elderly care. In Proceedings of the International Conference on Mobile Computing, Applications, and Services. Springer, 1--19.Google ScholarGoogle Scholar
  92. Pang Wu, Jiang Zhu, and Joy Ying Zhang. 2013. MobiSens: A versatile mobile sensing platform for real-world applications. Mob. Netw. Applic. 18, 1 (2013), 60--80.Google ScholarGoogle ScholarDigital LibraryDigital Library
  93. X. Xia, D. Lo, F. Zhu, X. Wang, and B. Zhou. 2013. Software internationalization and localization: An industrial experience. In Proceedings of the 18th International Conference on Engineering of Complex Computer Systems. 222--231. DOI:https://doi.org/10.1109/ICECCS.2013.40.Google ScholarGoogle Scholar
  94. Haoyi Xiong, Yu Huang, Laura E. Barnes, and Matthew S. Gerber. 2016. Sensus: A cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 415--426.Google ScholarGoogle Scholar

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          cover image ACM Transactions on Computing for Healthcare
          ACM Transactions on Computing for Healthcare  Volume 2, Issue 1
          Special Issue on Wearable Technologies for Smart Health: Part 2 and Regular Papers
          January 2021
          204 pages
          ISSN:2691-1957
          EISSN:2637-8051
          DOI:10.1145/3446563
          Issue’s Table of Contents

          Copyright © 2020 Owner/Author

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 30 December 2020
          • Accepted: 1 July 2020
          • Revised: 1 May 2020
          • Received: 1 December 2019
          Published in health Volume 2, Issue 1

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