Exploratory study of personal health information management using health literacy model

Purpose: The purpose of this paper is to empirically test whether individuals’ internal factors (prior knowledge, resources, and capability) and environmental factors (stimuli, limitation) have any influence on the development of personal health information management (PHIM) literacy skills and which constructs are statistically associated with general health-related outcomes. Design/methodology/approach: Survey responses were collected from Amazon’s Mechanical Turk (mTurk), a crowdsourcing internet service, in December 2013. A total of 578 responses were analyzed using partial-least squares structural equation modeling technique. Findings: The model as a whole exhibited 62.8 percent of variance in health-related outcomes. The findings suggest that prior knowledge has a direct effect on health literacy (HL) skills (H3: β=0.212, p<0.001). The PHIM stimuli (H4: β=0.475, p<0.001) have a direct impact on HL skills, and they have an indirect effect on the comprehension of stimuli (H6: β=0.526, p<0.001) through the mediator of stimuli and the knowledge variable. Research limitations/implications: One possible limitation of this study is that the study may include a highly technology literate group, as survey respondents were recruited from the online service mTurk. Practical implications: The study poses implications for further research and practice. This research was an exploratory work for further model development so future studies should investigate deeper into real personal health record (PHR) user groups (e.g. patients and caregivers). For example, studies by White and Horvitz (2009a, b) conducted real-time user studies that the authors could apply to the authors’ future PHR studies. Since the findings cannot be generalizable to these specific groups, similar research may be conducted. Using caregiver groups of PHR users in comparison to patient groups could determine the similarities and differences of their PHIM activities and related outcomes for optimal design of self-care management. Social implications: Further, it is suggested to conduct large scale, real-time-based studies using a PHR transaction log analysis to achieve conclusiveness and generalizability. Additionally, future studies should address not only diverse real-time user groups, but also various PHR data sources and their presentation issues. Originality/value: This study model offers an important perspective on PHIM and its causal pathway for use not only by patient educators and healthcare providers but also information providers, personal health record (PHR) system developers, and PHR users.