Exploratory study of personal health information management using health literacy model

Abstract

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.

Impact of contexts, resource types and perceptions on information management within the personal domain among college students

“…Moreover, participants are found to be least engaged in the health context, which is reinforced by results of the PCA that the health context stands out as a factor. College students are responsible for their own health while living apart from parents (Kim et al , 2018). However, these young and healthy students may not be interested in managing health information, as they reported not being active in searching and collecting health-related information (Kim et al , 2020; Park et al , 2006; Vader et al , 2011).…”

“…On the other hand, college students' management of health information shows different tendencies. The college period is a critical time for college students, as they transit to becoming independent and responsible for their own health management (Kim et al , 2018). However, they do not pay much attention to health information (Kim et al , 2020), although it seems they are aware of the importance of collecting medical records for their own safekeeping (Huvila et al , 2018).…”

Advancing health-related abilities and behaviors via health apps: a large-scale survey from self-regulation perspective

“…However, individuals without sufficient HIL have had difficulties interacting effectively with and adopting health technologies (i.e. health apps) in the context of health selfevaluation (Kim et al, 2018;Zhang et al, 2018). Although the interactive nature of health technologies brings new challenges like these, users' HILs can be improved with online application use (Spring, 2011).…”

“…With over 45,400 and 43,200 health apps accessible worldwide from the Apple App Store (Statista, 2020a) and Google Play (Statista, 2020b), respectively, the global health app market is valued at USD $40.05 billion by 2020 (Grand View Research, 2021) and is expected to grow at a compounded annual growth rate of 17.7% from 2021 to 2028 (Grand View Research, 2021). As several studies have alluded to the instrumental role of technological interventions in users' pursuit of long-term fitness goals, the exponential growth in health apps should be a cause for celebration (Hirvonen et al, 2016;Kim et al, 2018;Zhang et al, 2018). However, recent reports have documented that college students' physiques in America are declining due to the lack of physical activity and unhealthy lifestyle choices (American Council on Education, 2018; Drugwatch, 2020), a worrying trend that runs contrary to the popularity of health apps.…”

“…As several studies have alluded to the instrumental role of technological interventions in users' pursuit of long-term fitness goals, the exponential growth in health apps should be a cause for celebration (Hirvonen et al. , 2016; Kim et al. , 2018; Zhang et al.…”