Published on in Vol 20, No 5 (2018): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9330, first published .
Mapping of Crowdsourcing in Health: Systematic Review

Mapping of Crowdsourcing in Health: Systematic Review

Mapping of Crowdsourcing in Health: Systematic Review

Journals

  1. Borda A, Gray K, Fu Y. Research data management in health and biomedical citizen science: practices and prospects. JAMIA Open 2020;3(1):113 View
  2. Pratap A, Allred R, Duffy J, Rivera D, Lee H, Renn B, Areán P. Contemporary Views of Research Participant Willingness to Participate and Share Digital Data in Biomedical Research. JAMA Network Open 2019;2(11):e1915717 View
  3. Tuerk P, Schaeffer C, McGuire J, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10) View
  4. Pluye P, Granikov V, Tang D, Granikov V, Pluye P. Facilitators and barriers associated with the implementation of an innovative cross-disciplinary monitoring of the scientific literature: The Collaborative eBibliography on Mixed Methods (CeBoMM) – A research protocol. Education for Information 2020;36(1):81 View
  5. Petrović N, Moyà-Alcover G, Varona J, Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: A systematic literature review. Health Informatics Journal 2020;26(4):2446 View
  6. St John-Matthews J, Newton P, Grant A, Robinson L. Crowdsourcing in health professions education: What radiography educators can learn from other disciplines. Radiography 2019;25(2):164 View
  7. Bassi H, Lee C, Misener L, Johnson A. Exploring the characteristics of crowdsourcing: An online observational study. Journal of Information Science 2020;46(3):291 View
  8. van Niekerk L, Ongkeko A, Hounsell R, Msiska B, Mathanga D, Mothe J, Juban N, Awor P, Balabanova D. Crowdsourcing to identify social innovation initiatives in health in low- and middle-income countries. Infectious Diseases of Poverty 2020;9(1) View
  9. Wu D, Ong J, Tang W, Ritchwood T, Walker J, Iwelunmor J, Tucker J. Crowdsourcing Methods to Enhance HIV and Sexual Health Services: A Scoping Review and Qualitative Synthesis. JAIDS Journal of Acquired Immune Deficiency Syndromes 2019;82(3):S271 View
  10. Latkin C, Dayton L, Yi G, Konstantopoulos A, Boodram B. Trust in a COVID-19 vaccine in the U.S.: A social-ecological perspective. Social Science & Medicine 2021;270:113684 View
  11. Vermicelli S, Cricelli L, Grimaldi M. How can crowdsourcing help tackle the COVID‐19 pandemic? An explorative overview of innovative collaborative practices. R&D Management 2021;51(2):183 View
  12. Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. npj Digital Medicine 2021;4(1) View
  13. Nguyen V, Benchoufi M, Young B, Ghosn L, Ravaud P, Boutron I. A scoping review provided a framework for new ways of doing research through mobilizing collective intelligence. Journal of Clinical Epidemiology 2019;110:1 View
  14. Renn B, Hoeft T, Lee H, Bauer A, Areán P. Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the U.S. npj Digital Medicine 2019;2(1) View
  15. Ren C, Tucker J, Tang W, Tao X, Liao M, Wang G, Jiao K, Xu Z, Zhao Z, Yan Y, Lin Y, Li C, Wang L, Li Y, Kang D, Ma W. Digital crowdsourced intervention to promote HIV testing among MSM in China: study protocol for a cluster randomized controlled trial. Trials 2020;21(1) View
  16. Kennedy-Metz L, Mascagni P, Torralba A, Dias R, Perona P, Shah J, Padoy N, Zenati M. Computer Vision in the Operating Room: Opportunities and Caveats. IEEE Transactions on Medical Robotics and Bionics 2021;3(1):2 View
  17. Desai A, Warner J, Kuderer N, Thompson M, Painter C, Lyman G, Lopes G. Crowdsourcing a crisis response for COVID-19 in oncology. Nature Cancer 2020;1(5):473 View
  18. Wong S, Smith S, Jacova C. Older Adults With Subjective Cognitive Decline Worry About the Emotional Impact of Cognitive Test Results. Alzheimer Disease & Associated Disorders 2020;34(2):135 View
  19. Szmelter-Jarosz A, Rześny-Cieplińska J. Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective. Sustainability 2019;12(1):317 View
  20. Créquit P, Boutron I, Meerpohl J, Williams H, Craig J, Ravaud P. Future of evidence ecosystem series: 2. current opportunities and need for better tools and methods. Journal of Clinical Epidemiology 2020;123:143 View
  21. Sood E, Wysocki T, Alderfer M, Aroian K, Christofferson J, Karpyn A, Kazak A, Pierce J. Topical Review: Crowdsourcing as a Novel Approach to Qualitative Research. Journal of Pediatric Psychology 2021;46(2):189 View
  22. Wang , Xia , Li , Wang . A Bibliometric Analysis of Crowdsourcing in the Field of Public Health. International Journal of Environmental Research and Public Health 2019;16(20):3825 View
  23. Beto J, Metallinos-Katsaras E, Leung C. Crowdsourcing: A Critical Reflection on This New Frontier of Participant Recruiting in Nutrition and Dietetics Research. Journal of the Academy of Nutrition and Dietetics 2020;120(2):193 View
  24. Krusche M, Burmester G, Knitza J. Digital crowdsourcing: unleashing its power in rheumatology. Annals of the Rheumatic Diseases 2020;79(9):1139 View
  25. Porsdam Mann S, Savulescu J, Ravaud P, Benchoufi M. Blockchain, consent and prosent for medical research. Journal of Medical Ethics 2021;47(4):244 View
  26. Gardašević G, Katzis K, Bajić D, Berbakov L. Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare. Sensors 2020;20(13):3619 View
  27. Geng Y, Huang P, Huang Y. Crowdsourcing in Nursing Education: A Possibility of Creating a Personalized Online Learning Environment for Student Nurses in the Post-COVID Era. Sustainability 2021;13(6):3413 View
  28. Latkin C, Dayton L, Miller J, Yi G, Jaleel A, Nwosu C, Yang C, Falade-Nwulia O. Behavioral and Attitudinal Correlates of Trusted Sources of COVID-19 Vaccine Information in the US. Behavioral Sciences 2021;11(4):56 View
  29. Noel-Storr A, Redmond P, Lamé G, Liberati E, Kelly S, Miller L, Dooley G, Paterson A, Burt J. Crowdsourcing citation-screening in a mixed-studies systematic review: a feasibility study. BMC Medical Research Methodology 2021;21(1) View
  30. Chen D, Shi X, Song X, Chen Z, Zhang H. Phone-based Ambient Temperature Measurement with a New Confidence-based Truth Inference Model. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(4):1 View
  31. Glennie M, Dowden M, Grose M, Scolyer M, Superina A, Gardner K. Engaging Remote Aboriginal Communities in COVID-19 Public Health Messaging via Crowdsourcing. Frontiers in Public Health 2022;10 View
  32. Rädsch T, Reinke A, Weru V, Tizabi M, Schreck N, Kavur A, Pekdemir B, Roß T, Kopp-Schneider A, Maier-Hein L. Labelling instructions matter in biomedical image analysis. Nature Machine Intelligence 2023;5(3):273 View
  33. Ben-Sasson A, Jacobs K, Ben-Sasson E, Joshi R. The feasibility of a crowd-based early developmental milestone tracking application. PLOS ONE 2022;17(5):e0268548 View
  34. Latkin C, Dayton L, Miller J, Eschliman E, Yang J, Jamison A, Kong X. Trusted information sources in the early months of the COVID-19 pandemic predict vaccination uptake over one year later. Vaccine 2023;41(2):573 View
  35. Latkin C, Dayton L, Bonneau H, Bhaktaram A, Ross J, Pugel J, Latshaw M. Perceived Barriers to Climate Change Activism Behaviors in the United States Among Individuals Highly Concerned about Climate Change. Journal of Prevention 2023;44(4):389 View
  36. Ershad M, Rege R, Fey A. Adaptive Surgical Robotic Training Using Real-Time Stylistic Behavior Feedback Through Haptic Cues. IEEE Transactions on Medical Robotics and Bionics 2021;3(4):959 View
  37. Dayton L, Song W, Kaloustian I, Eschliman E, Strickland J, Latkin C. A longitudinal study of COVID-19 disclosure stigma and COVID-19 testing hesitancy in the United States. Public Health 2022;212:14 View
  38. Cheng A, Wen L, Li Y, Koyama T, Berry L, Pal T, Friedman D, Osterman T. Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care. JAMIA Open 2021;4(4) View
  39. Upshaw S, Jensen J, Giorgi E, Pokharel M, Lillie H, Adams D, John K, Wu Y, Grossman D. Developing skin cancer education materials for darker skin populations: crowdsourced design, message targeting, and acral lentiginous melanoma. Journal of Behavioral Medicine 2023;46(3):377 View
  40. Sha Y, Li C, Xiong Y, Hazra A, Lio J, Jiang I, Huang H, Kerman J, Molina J, Li L, Liang K, Gong D, Li Q, Wu S, Sherer R, Tucker J, Tang W. Co-creation using crowdsourcing to promote PrEP adherence in China: study protocol for a stepped-wedge randomized controlled trial. BMC Public Health 2022;22(1) View
  41. Latkin C, Dayton L, Coyle C, Yi G, Winiker A, German D. The association between climate change attitudes and COVID-19 attitudes: The link is more than political ideology✰,✰✰,★. The Journal of Climate Change and Health 2022;5:100099 View
  42. Latkin C, Hendrickson Z, Dayton L, Bonneau H. Political and Social Drivers of COVID-19 Prevention and Climate Change Behaviors and Attitudes. Climate 2023;11(3):53 View
  43. Gaughan M, Kwon M, Park E, Jungquist C. Nurses' Experience and Perception of Technology Use in Practice. CIN: Computers, Informatics, Nursing 2022;40(7):478 View
  44. Aguayo G, Goetzinger C, Scibilia R, Fischer A, Seuring T, Tran V, Ravaud P, Bereczky T, Huiart L, Fagherazzi G. Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study. Journal of Medical Internet Research 2021;23(12):e25743 View
  45. Latkin C, Dayton L, Scherkoske M, Countess K, Thrul J. What predicts climate change activism?: An examination of how depressive symptoms, climate change distress, and social norms are associated with climate change activism. The Journal of Climate Change and Health 2022;8:100146 View
  46. Guo Y, Xie H, Miao Y, Wang C, Jia X. FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on Blockchain. IEEE Transactions on Services Computing 2022;15(4):2060 View
  47. Han L, Tang W, Ritchwood T, Day S, Wei S, Bao H, John R, Kpokiri E, Mathanga D, Awor P, Juban N, Castro-Arroyave D, Ambil V, Xiong Y, Oppong E, Tucker J. Joint international consensus statement on crowdsourcing challenge contests in health and medicine: results of a modified Delphi process. BMJ Open 2021;11(11):e048699 View
  48. Latkin C, Dayton L, Miller J, Yi G, Balaban A, Boodram B, Uzzi M, Falade-Nwulia O. A longitudinal study of vaccine hesitancy attitudes and social influence as predictors of COVID-19 vaccine uptake in the US. Human Vaccines & Immunotherapeutics 2022;18(5) View
  49. Schlicher K, Schulte J, Reimann M, Maier G. Flexible, Self-Determined… and Unhealthy? An Empirical Study on Somatic Health Among Crowdworkers. Frontiers in Psychology 2021;12 View
  50. Zhang C, Guo Y, Jia X, Wang C, Du H. Enabling Proxy-Free Privacy-Preserving and Federated Crowdsourcing by Using Blockchain. IEEE Internet of Things Journal 2021;8(8):6624 View
  51. Taylor C, Flaks-Manov N, Ramesh S, Choe E. Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study. Journal of Medical Internet Research 2021;23(10):e19789 View
  52. Ghiringhelli C, Piras G, Arbia G, Mira A. Recursive Estimation of the Spatial Error Model. Geographical Analysis 2023;55(1):90 View
  53. Latkin C, Dayton L, Coyle C, Yi G, Lee D, Winiker A. The Relationship between Social Norms, Avoidance, Future Orientation, and Willingness to Engage in Climate Change Advocacy Communications. International Journal of Environmental Research and Public Health 2021;18(24):13037 View
  54. Latkin C, Dayton L, Lee D, Yi G, Uzzi M. Correlates of Levels of Willingness to Engage in Climate Change Actions in the United States. International Journal of Environmental Research and Public Health 2021;18(17):9204 View
  55. Olsen R, Genét M, Konge L, Bjerrum F. Crowdsourced assessment of surgical skills: A systematic review. The American Journal of Surgery 2022;224(5):1229 View
  56. Silva G, Schwamm L. Advances in Stroke: Digital Health. Stroke 2022;53(3):1004 View
  57. Evans L, Gomez O, Jiménez D, Williamson H, Carver A, Parthasarathy S, Sabo S. Engaging Youth and Young Adults in the COVID-19 Pandemic Response via the “It’s Our Turn” Crowdsourcing Contest. International Journal of Environmental Research and Public Health 2023;20(6):5112 View
  58. Schucht P, Mathis A, Murek M, Zubak I, Goldberg J, Falk S, Raabe A. Exploring Novel Innovation Strategies to Close a Technology Gap in Neurosurgery: HORAO Crowdsourcing Campaign. Journal of Medical Internet Research 2023;25:e42723 View
  59. Ben-Sasson A, Jacobs K, Ben-Sasson E. Early childhood tracking application: Correspondence between crowd-based developmental percentiles and clinical tools. Health Informatics Journal 2023;29(1):146045822311646 View
  60. Akbarpour M, Tawk K, Frank M, Gomez A, Mostaghni N, Abouzari M. Assessment of laryngologists' ratings on physician review websites. World Journal of Otorhinolaryngology - Head and Neck Surgery 2024;10(1):1 View
  61. Rochette A, Thomas A, Salbach N, Vachon B, Menon A, Poissant L, Boutin M, Grad R, Pluye P. Expected Health Benefits as the Ultimate Outcome of Information Available on Stroke Engine, a Knowledge Translation Stroke Rehabilitation Website: Web-Based Survey. JMIR Rehabilitation and Assistive Technologies 2023;10:e44715 View
  62. Kuosmanen E, Huusko E, van Berkel N, Nunes F, Vega J, Goncalves J, Khamis M, Esteves A, Ferreira D, Hosio S. Exploring crowdsourced self-care techniques: A study on Parkinson’s disease. International Journal of Human-Computer Studies 2023;177:103062 View
  63. Arbia G, Nardelli V. Using Web-Data to Estimate Spatial Regression Models. International Regional Science Review 2024;47(2):204 View
  64. Monaghan J, Backholer K, McKelvey A, Christidis R, Borda A, Calyx C, Crocetti A, Driessen C, Zorbas C. Citizen science approaches to crowdsourcing food environment data: A scoping review of the literature. Obesity Reviews 2023;24(11) View
  65. DeSouza A, Wang D, Wong J, Furlan A, Hogg-Johnson S, Macedo L, Mior S, Côté P. Prevalence of Unmet Rehabilitation Needs Among Canadians Living With Long-term Conditions or Disabilities During the First Wave of the COVID-19 Pandemic. Archives of Physical Medicine and Rehabilitation 2024;105(2):268 View
  66. Sescleifer A, Francoisse C, Osborn T, Rector J, Lin A. Seeing Cleft Lip from a New Angle: Crowdsourcing to Determine whether Scar Severity or Lip Angle Matters More to the General Public. Plastic & Reconstructive Surgery 2023;152(1):126e View
  67. Konstantopoulos A, Dayton L, Latkin C. The politics of vaccination: a closer look at the beliefs, social norms, and prevention behaviors related to COVID-19 vaccine uptake within two US political parties. Psychology, Health & Medicine 2024;29(3):589 View
  68. Zhang E, Trujillo R, Templeton J, Poellabauer C. A Study on Mobile Crowd Sensing Systems for Healthcare Scenarios. IEEE Access 2023;11:140325 View
  69. Latkin C, Dayton L, Winiker A, Countess K, Hendrickson Z. ‘They Talk about the Weather, but No One Does Anything about It’: A Mixed-Methods Study of Everyday Climate Change Conversations. International Journal of Environmental Research and Public Health 2024;21(3):279 View
  70. Ivanova D, Katsaounis P, Votis K. Increasing the Value of Real-World Crowdsourcing Health Data with e-MetaBio, a Novel Patient-Centric IT Infrastructure. Innovations in Digital Health, Diagnostics, and Biomarkers 2024;4(2024):15 View

Books/Policy Documents

  1. Song M, Tabi K, Krausz M. Innovations in Global Mental Health. View
  2. Hall D, Hibbert A, Vesala M, Kerr M, Harrison S. Tinnitus - An Interdisciplinary Approach Towards Individualized Treatment: From Heterogeneity to Personalized Medicine. View
  3. Sathya D. , Sudha V. , Jagadeesan D. . Handbook of Research on Applications and Implementations of Machine Learning Techniques. View
  4. Song M, Tabi K, Krausz M. Innovations in Global Mental Health. View
  5. Kasturi N, Totad S, Ghosh G. Emerging Technologies in Data Mining and Information Security. View
  6. Sathya D. , Sudha V. , Jagadeesan D. . Research Anthology on Machine Learning Techniques, Methods, and Applications. View
  7. Stojmenović M. Mobile Crowdsourcing. View