Identifying Challenges and Barriers in Wearable Medical Devices Adoption through Text Mining

The use of smart wearable devices in health care has proliferated rapidly in the past few years in diagnosis, prevention, prediction and treatment. This has been made possible through the continuous technological advances like artificial intelligence, internet of things that allows continuous 24×7 monitoring of health, activity, mobility, and mental status. In spite of their benefits there are still challenges related to their adoption. If the challenges and barriers in adoption are identified it may help to expand their increased adoption. The current study adopts text mining approach through R to retrieve information from articles published in Pubmed and Google scholar. Based on the occurrence frequency of the words six major themes were identified as notable challenges and barriers. They are related to sensor, devices, data, monitoring and system. The other challenges identified were related to security, lack of regulation, risk of data loss, accuracy and ethics. The results prove that it is possible to extract information through textmining.