A Classification Framework of Mobile Health CrowdSensing Research

Abstract

Mobile Health CrowdSensing (MHCS) is the use of sensor-based smart mobile devices to generate, collect, share and analyse real-time patient data from a population for improving health. The purpose of the paper is to report on a scoping review of research conducted on MHCS using a proposed classification framework. The framework has five categories of attributes (purpose; adoption and barriers; system attributes; software; and benefits and outcomes). Repositories searched were Medline/PubMed, Embase/Science Direct, PsycINFO, EBSCOHOST, IEEE, ACM Digital and accessible University library repositories from January 1998 to December 2018. Certain exclusion criteria were applied. 'Mobile crowd sensing' and 'mobile crowdsensing' keywords were separately used as search terms. Thirteen studies met the inclusion criteria. The findings revealed that MHCS applications in healthcare are scant. Most MHCS studies were from conference proceedings rather than traditional journals because of the fast-pace of technology-driven research. Healthcare research is costly and time-consuming thus there is a need to adopt MHCS as it can contribute towards low cost research due to the affordable devices used in generating, collecting and providing access to real-time data.