Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review (Preprint)

Abstract

BACKGROUND Diabetes mellitus is a metabolic disorder suffered by hundreds of millions of people worldwide and causing several million deaths every year. Such a dramatic scenario puts quite some pressure on administrations, care services and the scientific community to seek novel solutions that may help controlling and dealing effectively with this condition and its consequences. OBJECTIVE This study is aimed at reviewing the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development and/or evolution of diabetes mellitus. METHODS A systematic review of articles published between January 2010 and July 2020 was performed following PRISMA guidelines. Manuscripts indexed in Web of Science and SCOPUS databases were included if they involved the measurement of diabetes-related parameters, such as blood glucose level, performed physical activity or feet condition, via wearable or mobile devices. RESULTS The search yielded 1587 articles. Altogether, 26 publications met the eligibility criteria and were included in the review. Studies used predominantly wearable devices for monitoring diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and the heart rate monitor. Most studies applied some kind of processing to the collected data mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level. CONCLUSIONS The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit diabetes patients, healthcare professionals and researchers. To evolve this area future research must pay special attention to privacy and security issues, the use of new emerging sensor technologies, and the combination of mobile and clinical data for a holistic and seamless understanding of the patient's health state.