JMIR Publications

ABSTRACT

Background:

The recent surge in clinical and non-clinal health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots.

Objective:

To reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using a bibliometric analysis.

Methods:

We collected 8,281 articles published between 2009 and 2018 from the Web of Science database. Knowledge evolution analysis (KEA) framework was used to analyse the evolution of PHD research. The KEA framework is a bibliometric approach that is based on three knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence.

Results:

The findings show that the focus of PHD research has evolved from medicine-centric to technology-centric to human-centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence, has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (e.g., attitude, satisfaction, education) are also receiving more attention.

Conclusions:

Our analysis shows that PHD research has the potential to provide value-based healthcare in the future. All stakeholders should be educated about the AI technology to promote value generation through PHD. Moreover, technology developers and healthcare institutions should take human factors into consideration to facilitate effective adoption of PHD related technology.