| Literature DB >> 34946466 |
Xiaoyun Jia1,2, Yan Pang3, Liangni Sally Liu4.
Abstract
The last five years have seen a leap in the development of information technology and social media. Seeking health information online has become popular. It has been widely accepted that online health information seeking behavior has a positive impact on health information consumers. Due to its importance, online health information seeking behavior has been investigated from different aspects. However, there is lacking a systematic review that can integrate the findings of the most recent research work in online health information seeking, and provide guidance to governments, health organizations, and social media platforms on how to support and promote this seeking behavior, and improve the services of online health information access and provision. We therefore conduct this systematic review. The Google Scholar database was searched for existing research on online health information seeking behavior between 2016 and 2021 to obtain the most recent findings. Within the 97 papers searched, 20 met our inclusion criteria. Through a systematic review, this paper identifies general behavioral patterns, and influencing factors such as age, gender, income, employment status, literacy (or education) level, country of origin and places of residence, and caregiving role. Facilitators (i.e., the existence of online communities, the privacy feature, real-time interaction, and archived health information format), and barriers (i.e., low health literacy, limited accessibility and information retrieval skills, low reliable, deficient and elusive health information, platform censorship, and lack of misinformation checks) to online health information seeking behavior are also discovered.Entities:
Keywords: health information consumers; online HISB; online health information seeking behavior; social
Year: 2021 PMID: 34946466 PMCID: PMC8701665 DOI: 10.3390/healthcare9121740
Source DB: PubMed Journal: Healthcare (Basel) ISSN: 2227-9032
Figure 1The flowchart of records retrieved, removed, and included.
Figure 2The number of reviewed papers since 2016.
The details of the articles reviewed.
| Authors | Publication Year | Title | Journal | Research Design | Data Collection Method | Sample Number | Subjects (Age Group/Sex/etc.) | Location | Data Analysis |
|---|---|---|---|---|---|---|---|---|---|
| Ukonu & Ajaebili, 2021 [ | 2021 | Socio-cultural determinants of women’s health information opportunities in Nsukka, southeast Nigeria | Asian Women | Quantitative | Survey | Reproductive-age women | America | Descriptive and correlation analysis | |
| Boyce et al., 2021 [ | 2021 | Exploring the factors in information seeking behavior: a perspective from multinational COPD online forums | Health Promotion International | Quantitative | Survey | Participants with chronic obstructive pulmonary disease (COPD). | America & other countries such as New Zealand, Australia, South Africa, the UK, Philippines, etc. | PLS-SEM (partial least squares structural equation modeling) | |
| Özkan et al., 2021 [ | 2021 | The relationship between health literacy level and media used as a source of health-related information | HLRP: Health Literacy Research and Practice | Quantitative | Survey | Turkish citizen | Turkey | Descriptive and linear regression analysis | |
| Augustaitis et al., 2021 [ | 2021 | Online transgender health information seeking: facilitators, barriers and future directions | Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems | Qualitative | Online focus groups | Trans and/or nonbinary adults | America | Inductive open coding approach | |
| Demirci et al., 2021 [ | 2021 | Socio-demographic characteristics affect health information seeking on the Internet in Turkey | Health Information & Libraries Journal. | Quantitative | Survey | Turkish | Turkey | Logistic regression | |
| Ghahramani & Wang, 2020 [ | 2020 | Impact of smartphones on quality of life: a health information behavior perspective | Information Systems Frontiers | Quantitative | Survey | American adults | America | Pearson’s correlation analysis, Preacher and Hayes Process macro | |
| Lee et al., 2020 [ | 2020 | Communication about health information technology use between patients and providers | Journal of General Internal Medicine | Quantitative | Survey | Adult residents in 34 Indiana counties with higher cancer mortality rates than the state average | America | Descriptive statistics and multivariable logistic regression | |
| Lee, 2020 [ | 2020 | Which individual characteristics influence mothers’ health information-seeking behavior? | Journal of the Korean Society for Library and Information Science | Quantitative | Survey | Among mothers of healthy infants and toddlers (currently living in the US or Korea) | America & Korea | Ordinal regression analyzes | |
| Bernadas & Jiang, 2019 [ | 2019 | Explaining online health information seeking of foreign domestic workers: a test of the comprehensive model of information seeking | Health and Technology | Quantitative | Survey | Filipino female foreign domestic workers (WFDs) in HK | Hong Kong | Multiple OLS regression | |
| Nangsangna & Vroom, 2019 [ | 2019 | Factors influencing online health information seeking behavior among patients in Kwahu West Municipal, Nkawkaw, Ghana | Online Journal of Public Health Informatics | Quantitative | Survey | Ghanaian residents who are over 16 | Ghana | Descriptive statistics, Chi-square test, and logistic regression | |
| Zhang et al., 2019 [ | 2019 | Why do patients follow physicians’ advice? The influence of patients’ regulatory focus on adherence: An empirical study in China | BMC Health Services Research | Quantitative | Survey | Chinese patients with health information seeking experience and hospital treatment experience | China | Structural equation modeling and confirmatory factor analysis | |
| LaValley et al., 2017 [ | 2017 | Where people look for online health information | Health Information & Libraries Journal | Quantitative | Survey | American adults | America | Regression analysis | |
| Maon et al., 2017 [ | 2017 | Online health information seeking behavior pattern | Advanced Science Letters | Quantitative | Survey | Malaysian adults | Malaysia | Descriptive analysis | |
| Osei Asibey et al., 2017 [ | 2017 | The Internet use for health information seeking among Ghanaian university students: A cross-sectional study | International Journal of Telemedicine and Applications | Quantitative | Survey | Ghanaian university students | Ghana | Descriptive analysis | |
| Manganello et al., 2017 [ | 2017 | The relationship of health literacy with use of digital technology for health information: implications for public health practice | Journal of Public Health Management and Practice | Quantitative | Survey | New York State adult residents | America | A weighted analysis | |
| Sultan et al., 2017 [ | 2017 | Health information seeking behavior of college students in the sultanate of Oman | Khyber Medical University Journal | Quantitative | Survey | Omani college students (adults) | Oman | Descriptive statistics and chi-square tests | |
| Kim et al., 2017 [ | 2017 | Seeking medical information using mobile Apps and the Internet: Are family caregivers different from the general public? | Journal of Medical Systems | Quantitative | Survey | American adults | America | Multivariate logistic regression | |
| Xuexia et al., 2016 [ | 2016 | Analysis of barriers to health information seeking and utilizing in patients with diabetes | Cross-Cultural Communication | N/A | N/A | N/A | N/A | China | N/A |
| Obasola & Agunbiade, 2016 [ | 2016 | Online health information seeking pattern among undergraduates in a Nigerian University | SAGE Open | Quantitative | Survey | Nigerian undergraduate students | Nigeria | Descriptive analysis | |
| Shneyderman et al., 2016 [ | 2016 | Health information seeking and cancer screening adherence rates | Journal of Cancer Education | Quantitative | Survey | Did not indicate | American adults | America | Descriptive statistics and regression modeling |
Figure 3The research model of Zhang et al. [29]. In this model, health information consumers with different focuses were found to seek different online health information.
Figure 4The partial research model of Ghahramani and Wang [11]. In this model, online health information seeking behavior was found to mediate the relationship between smartphone use and quality of life.
Figure 5The research model of Boyce et al. [22]. In this model, online health information seeking behavior was found to be influenced by perceived usefulness, perceived ease of use and sense of self-worth.