Quantified-self technology in promoting well-being: The digitalization of holistic well-being models

Abstract

Objectives The main objectives of this thesis were to examine the current consumer quantified-self technology in use today, how the technology can be used for well-being purposes, and the different factors in play when building the future well-being models. The research was conducted as an examination of literature.

Summary The main applications of quantified-self technology are activity trackers, sleep trackers, and habit trackers. EEG (electroencephalogram) sensors are also used, but less popular. There are several initiatives such as Google Fit and Neosmart Health that use this technology with AI (artificial intelligence) and ML (machine learning) to create constantly developing well-being models. The technology is developing, but science is still yet to prove that using quantified-self technology works to improve users’ health. Furthermore, the applications are data heavy and issues with data ethics need to be sort out before wide commercial health applications can be assembled.

Conclusions Quantified-self technology is still in its beginning phases. The potential of the technology with health care applications is notable in theory, but studies in randomized settings need to be conducted to prove the health benefits of using such technology. A working model for data ownership and privacy also is required for revolutionary health care applications. Until there is enough science and regulation behind quantified-self technology, industry pioneers will continue building new iterations of the technology and pushing it to be the future of health care.