Online Health Information Seeking Behaviors Among People With Diabetes: A Scoping Review

Abstract

Introduction

Online health information seeking (OHIS) behavior has emerged as a common practice among people with diabetes. This scoping review aimed to map the literature on OHIS behaviors among people with diabetes, specifically their patterns of digital platform use and content preferences.

Methods

This review was guided by the framework of Arksey and O'Malley. A search was conducted in November and December 2024 across the following databases: Cochrane Library, PubMed, Web of Science, CINAHL, Embase, SinoMed, CNKI, and Wanfang Data. Qualitative, quantitative, and mixed-methods studies were considered for inclusion.

Results

A total of 41 eligible articles published between 2005 and 2024 were included. Three research questions were addressed: types and sources, incidence, influencing factors and barriers associated with OHIS behaviors. Moreover, commonly sought information concerned lifestyle management, peer support, and diabetes management, primarily from general internet, social media/blogs, and health websites. Socio-demographic (e.g., age, gender, occupation), diabetes related characteristics (e.g., diabetes type, disease duration), and motivational factors (e.g., activation) were identified as key factors influencing OHIS behaviors among people with diabetes. The incidence varied across survey times and continents, ranging from 11.30% to 96.00%. Additionally, three types of barriers to OHIS behaviors were identified, namely individual-related (e.g., interest, cost), equipment (e.g., unorganized information, internet design), and social related barriers (e.g., lack of social support, living area).

Conclusions

This review reveals a substantial gap in understanding of OHIS behaviors among people with diabetes. Notable barriers to effective OHIS persist, particularly those related to information usability and accessibility. Future research should prioritize developing interventions to enhance the accuracy, readability, and personalization of online diabetes information and optimize search experiences. Moreover, efforts to improve the comprehension of complex medical content and deliver disease-stage-specific digital information will be essential to empower people with diabetes in effectively retrieving and using high-quality online health information.

Keywords

online health information seeking diabetes mellitus social media health information behavior scoping review

Introduction

In today’s digitally connected era, the Internet has transformed access to health information and become a valuable resource for the managing chronic conditions such as diabetes (Peimani et al., 2024; Yang et al., 2023). Compared with traditional channels, the internet offers health information that is more accessible, multi-dimensional, and continuously updated (Link et al., 2021). Online health information seeking (OHIS) behavior refers to the deliberate acquisition of health-related information from digital sources, a process that spans initial search triggers, employed strategies, targeted information types, and final outcomes (Chen et al., 2025; Elnegaard et al., 2017; Wang et al., 2021). In this process, individuals actively utilize various online platforms and sources to acquire relevant knowledge (Stifjell et al., 2025). Internet use affects the physical and psychological health, and the decision-making processes of most people with chronic conditions (Alhadreti, 2023; Welch Cline et al., 2007). Therefore, investigating OHIS behaviors of people with diabetes is essential, as it provides a foundation for developing targeted health interventions and resources align with their specific needs and habits.

Diabetes mellitus affects approximately 425 million individuals worldwide and poses a growing health burden (Lee et al., 2020). As a chronic condition defined by elevated blood glucose, its management depends on patient education, self-care, and clinical support (Bosch-Frigola et al., 2022; Ferraris et al., 2023). Due to limited annual contact with healthcare providers (Korenhof et al., 2022), people with diabetes must independently develop self-management skills and motivation. Online health resources offer a supplementary pathway that promotes autonomy by overcoming access barriers (Hughes et al., 2024). Hence, OHIS behaviors are regarded as a means to empower people with diabetes, improve public health outcomes, and alleviate pressure on healthcare systems. Nevertheless, it remains uncertain to what extent people with diabetes benefit from digital technologies for health information seeking, including the Internet, social media, and online health platforms (Zhao et al., 2022a). Reported OHIS prevalence varies widely, from 38.40% in Malaysia (Leelavathi et al., 2018) to 64.49% in a US-based study (Eke et al., 2022), likely due to differences in population characteristics, digital infrastructure, and measurement methods. OHIS behaviors are also influenced by personal, demographic, and social factors, including health literacy (Wang et al., 2021). In light of the rapidly evolving digital environment, identifying the characteristics of OHIS behaviors among people with diabetes has major implications for practice and research.

People with different types of diabetes seek both common and diverse health information online reflecting the heterogeneous nature of diabetes management (Kuske et al., 2017). For instance, those with type 1 diabetes mellitus (T1DM) predominantly seek technical information on intensive insulin therapy, continuous glucose monitoring, and artificial pancreas systems. Whereas those with type 2 diabetes mellitus (T2DM) prioritize lifestyle modifications, dietary guidance, medication adherence, and weight management (Jamal et al., 2015; Leelavathi et al., 2018; Litchman, Edelman, & Donaldson, 2018). These differing needs stems from the distinct pathophysiological mechanisms and treatments of each diabetes subtype. Although existing reviews have advanced understanding of OHIS behaviors in general chronic disease or specific age groups (Stifjell et al., 2025), few have focused on people with diabetes who face daily, life-sustaining self-management decisions (Kuske et al., 2017). Recent reviews have examined specific aspects of digital engagement, such as online communities for psychosocial needs (Oser et al., 2020) or social media use and healthcare professional relationships (Kjærulff et al., 2023), but none have integrated the motivational, behavioral, socio-technical, and relational dimensions of OHIS in people with diabetes within a unified framework. Bibliometric analyses confirm OHIS in diabetes as a rapidly growing research hotspot, highlighting the many original studies await synthesis (Li et al., 2015). This scoping review is therefore timely and essential, integrating diverse diverse studies to advance a holistic understanding of OHIS among people with diabetes and provide a foundation for tailored online resources and support strategies throughout their disease trajectory.

A scoping review is an effective method for summarizingexisting research, and disseminating findings, and identifying knowledge gaps in a particular field (Foster et al., 2024). Given the breadth of this topic, such review is particularly warranted. Accordingly, this scoping review aimed to map the literature on OHIS behaviors among individual with diabetes, specifically their patterns of digital platform use and content preferences. The scoping review was guided by the following purposes: What types of online OHIS behaviors do people with diabetes engage in, and what sources do they frequently utilize? What is the incidence of OHIS behaviors among people with diabetes? What factors and barriers influence OHIS behaviors in this population?

Methods

This scoping review was performed in accordance with the Arksey and O’Malley’s framework (Arksey & O’Malley, 2005). The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) checklist and a PRISMA flow diagram were strictly followed (Tricco et al., 2018). To begin, the primary research question for this scoping review, which focused on the OHIS behaviors of people with diabetes, was formulated. Secondly, an iterative and non-linear literature search was performed across multiple databases to identify relevant articles, including gray literature. Then, the inclusion and exclusion criteria were progressively developed as familiarity with the literature increased throughout the screening and review process, and are listed in Supplementary File A. Articles were screened for eligibility based on those established criteria. Next, a data charting form was used to record and synthesize the data. Finally, the current state of research was scrutinized and analyzed, knowledge gaps were identified, and directions for future research were proposed. The protocol of this scoping review was registered on the Open Science Framework (OSF) (https://osf.io/yx7vs/).

Literature Search

Two researchers performed a comprehensive electronic search in both English and Chinese databases, including Cochrane Library, PubMed, Web of Science, CINAHL (EBSCO), Embase (Ovid), SinoMed, CNKI, and Wanfang Data. The database search was supplemented with snowball searching, which involved reviewing reference lists and tracking citations forward in time. This search was conducted during November and December 2024. To explore this under-researched topic, no publication date restrictions were applied. The strategies used for this scoping review, including Medical Subject Headings (MeSH) terms, free-text terms, and specific search queries, are listed in Supplementary File B. After the searches, all identified citations were collected and imported into NoteExpress software. Meanwhile, duplicated records or citations unrelated to the OHIS behaviors of people with diabetes were excluded. Two researchers independently screened each title and abstract to assess their relevance based on the predefined inclusion and exclusion criteria. A third researcher arbitrated discrepancies.

Study Identification

The screening process was performed based on the eligibility criteria. Initially, the database search strategies identified 7,070 publications, with 12 additional records identified through snowball searching. After duplicate removal, 3,850 records remained. Two reviewers independently screened the titles and abstracts of these records, resulting in 99 articles selected for full-text assessment. After assessing the full texts against the eligibility criteria, 41 articles were included in the scoping review. Detailed search results are illustrated in Figure 1. Some of the included articles also addressed other chronic diseases. Nevertheless, given that the majority of studies focused on people with diabetes, their findings were incorporated into this review.

Figure 1.

Selection process for study inclusion

Data Extraction and Summary

Two reviewers independently performed data extraction according to the standardized data extraction protocol, following which records were imported into Excel (Microsoft Corporation). Extracted data included the author(s), title, study location, publication year, journal name, design, participant characteristics, and outcomes (eg. incidence of OHIS behaviors, sources of health information, information types, influencing factors, barriers, and interventions), with discrepancies resolved by a third researcher. Article selection, screening, and data extraction were completed by January 2025. The data were summarized and presented in Tables 1 and 2.

Table 1.

Characteristics of Included Reviews (n=41)

Author ID	Year	Country	Methodological	Subjects
Jackson et al.	2005	USA	Cross-sectional study	Urban African Americans with T2DM
Yamamoto et al.	2011	Japan	Cross-sectional study	Islet Transplantation in Patients with T1DM
Shaw & Johnson	2011	USA	Cross-sectional study	Diabetes
Hyman et al.	2012	Canada	Cross-sectional study	Diabetes
Jamal et al.	2015	Saudi Arabia	Cross-sectional study	T2DM
Kalantzi et al.	2015	Greece	Cross-sectional study	Type 1 or 2 DM
Lui et al.	2015	Australia	Longitudinal quantitative study	Diabetes
Mills et al.	2015	UK	Randomized clinical study	T2DM
Giménez-Pérez et al.	2016	Spain	Cross-sectional study	T1DM
Vitger et al.	2017	Denmark	Cross-sectional study	T2DM
Leelavathi et al.	2018	Malaysia	Cross-sectional study	T2DM
Alotaibi & Mohd	2021	Saudi Arabia	Cross-sectional study	Diabetes
Litchman et al.	2018a	USA	Cross-sectional study	T1DM or T2DM, or LADA
Eke et al.	2022	USA	Cross-sectional study	Diabetes
Kebede & Pischke	2019	Germany	Cross-sectional study	T1DM,T2DM
Rasmussen et al.	2007	Australia	Grounded Theory	Young women with Diabetes
Longo et al.	2010	USA	Focus Groups	Diabetes
Wilson	2013	UK	Qualitative study (Thematic analysis)	T2DM
Meyfroidt et al.	2013	Belgium	Focus Groups	Diabetes
Connolly & Crosby	2014	USA	Focus Groups	Diabetes
Fergie et al.	2016	UK	Semi-structured Interviews	Young adults (aged 18–30) with diabetes
Hughes et al.	2021	USA	Qualitative study (Thematic analysis)	T1DM
Kjærulff & Langstrup	2023	Denmark	Semi-structured Interviews	T1DM, T2DM
Newton et al.	2012	Denmark	Qualitative study (Thematic analysis)	T2DM
Litchman et al.	2018b	USA	Semi-structured Interviews	Older adults with T1DM, T2DM, and LADA
Greene et al.	2011	USA	Content Analysis (A Qualitative Evaluation)	Diabetes
Kjærulff et al.	2024	Denmark	Online Ethnographic Study	T1DM, T2DM
Lindgreen et al.	2024	Denmark	Online Observation	T2DM
Årsand et al.	2019	Norway	Content Analysis (Qualitative Method)	Diabetes
Zhang et al.	2018	USA	Content Analysis (Qualitative Method)	Diabetes
Pounds et al.	2018	UK	Content Analysis (Discourse Analysis)	Diabetes
Lewis et al.	2018	UK	Content Analysis (Semantic Network Analysis)	Older Adults With T2DM
Alzahrani et al.	2022	UK	Content Analysis (Qualitative Method)	Diabetes
Allen-Taylor et al.	2022	UK	Content Analysis (Thematic Analysis)	T2DM
Yao et al.	2022	China	Content Analysis (Mixed- Methods)	Diabetes
Da Moura Semedo et al.	2023	UK	Content Analysis (Mixed Methods)	T2DM, T2DM under insulin, T2DM in remission
Weymann et al.	2016	Germany	Content Analysis (Mixed- Methods)	T2DM
Tenderich et al.	2019	USA	Content Analysis (Netnography)	Diabetes
Abedin et al.	2017	Canada	Content Analysis (A Netnographic Analysis)	Diabetes
Stellefson et al.	2019	USA	Content Analysis (Qualitative Method)	Diabetes
Gabarron et al.	2020	Norway	Content Analysis (Qualitative Method)	Diabetes

Note. T1DM = Type 1 Diabetes Mellitus; T2DM = Type 2 Diabetes Mellitus; LADA = Latent Autoimmune Diabetes of Adulthood, DOC = Diabetes Online Community.

Table 2.

Types of Online Health Information Mentioned in the Articles (n=41)

Types of health information	Examples	Number of articles (n)	Study design
Types of health information	Examples	Number of articles (n)	Quantitative studies	Qualitative studies	Content analysis
Lifestyle management	Lifestyle modification for diabetes (diet & exercise)	22	Jamal et al., 2015; Kalantzi et al., 2015; Kebede & Pischke, 2019; Leelavathi et al., 2018; Litchman, Edelman, & Donaldson, 2018; Shaw & Johnson, 2011; Vitger et al., 2017.	Fergie et al., 2016; Hughes et al., 2021; Kjærulff et al., 2024; Kjærulff & Langstrup, 2023; Litchman, Rothwell, & Edelman, 2018; Wilson, 2013.	Allen-Taylor et al., 2022; Årsand et al., 2019; Gabarron et al.,2020; Lewis et al., 2018; Stellefson et al., 2019; Tenderich et al., 2019; Weymann et al., 2016; Yao et al., 2022; Zhang et al., 2018.
Peer support	solicited advice from peers with similar experiences	19	Kebede & Pischke, 2019; Lewis et al., 2018; Litchman, Edelman, & Donaldson, 2018; Shaw & Johnson, 2011.	Allen-Taylor et al., 2022; Fergie et al., 2016; Hughes et al., 2021; Kjærulff et al., 2024; Lindgreen et al., 2024; Rasmussen et al., 2007; Tenderich et al., 2019.	Årsand et al., 2019; Da Moura Semedo et al., 2023; Gabarron et al., 2020; Greene et al., 2011; Pounds et al., 2018; Stellefson et al., 2019; Weymann et al., 2016; Zhang et al., 2018.
Diabetes information	Causes of diabetes, What is diabetes	17	Jamal et al., 2015; Leelavathi et al., 2018; Lewis et al., 2018; Litchman, Edelman, & Donaldson, 2018; Mills et al., 2015; Kebede & Pischke, 2019; Shaw & Johnson, 2011; Vitger et al., 2017.	Kjærulff et al., 2024; Litchman, Rothwell, & Edelman, 2018; Newton et al., 2012; Rasmussen et al., 2007; Wilson, 2013.	Abedin et al., 2017; Årsand et al., 2019; Greene et al., 2011; Yao et al., 2022.
Treatment details	Insulin or pump treatment	15	Alotaibi & Mohd, 2021; Jamal et al., 2015; Leelavathi et al., 2018; Lewis et al., 2018; Litchman, Edelman, & Donaldson, 2018; Shaw & Johnson, 2011.	Kjærulff et al., 2024; Kjærulff & Langstrup, 2023; Rasmussen et al., 2007; Tenderich et al., 2019; Wilson, 2013.	Alzahrani et al., 2022; Årsand et al., 2019; Gabarron et al., 2020; Weymann et al., 2016.
Medications/devices	Side effect of diabetes medication	14	Jamal et al., 2015; Leelavathi et al., 2018; Lewis et al., 2018; Litchman, Edelman, & Donaldson, 2018; Kebede & Pischke, 2019.	Hughes et al., 2021; Kjærulff et al., 2024; Lindgreen et al., 2024; Litchman, Rothwell, & Edelman, 2018; Wilson, 2013.	Årsand et al., 2019; Stellefson et al., 2019; Yao et al., 2022; Zhang et al., 2018; .
General health information	Lose weight	9	Alotaibi & Mohd, 2021; Jamal et al., 2015; Lewis et al., 2018; Mills et al., 2015; Shaw & Johnson, 2011.	Tenderich et al., 2019; Wilson, 2013.	Weymann et al., 2016; Yao et al., 2022.
Complications	Foot complications in diabetes	7	Jamal et al., 2015; Kalantzi et al., 2015; Leelavathi et al., 2018.	Wilson, 2013.	Årsand et al., 2019; Weymann et al., 2016; Yao et al., 2022.
Symptoms	Symptoms of diabetes/ hypoglycaemia	7	Jamal et al., 2015; Kalantzi et al., 2015; Leelavathi et al., 2018.	Fergie et al., 2016; Litchman, Rothwell, & Edelman, 2018; Wilson, 2013.	Newton et al., 2012.
Health news/ policies	Financial assistance	7	Kebede & Pischke, 2019; Shaw & Johnson, 2011.	Rasmussen et al., 2007.	Årsand et al., 2019; Gabarron et al., 2020; Greene et al., 2011; Stellefson et al., 2019.
Express emotions	Venting, stigma	7	Litchman, Edelman, & Donaldson, 2018.	Kjærulff & Langstrup, 2023; Litchman, Rothwell, & Edelman, 2018; Rasmussen et al., 2007; Tenderich et al., 2019.	Da Moura Semedo et al., 2023; Zhang et al., 2018.
Communicate with physicians	Communicate with physicians online about the condition	4	Alotaibi & Mohd, 2021; Kebede & Pischke, 2019; Litchman, Edelman, & Donaldson, 2018.	/	Årsand et al., 2019.
Financial information	Insurance assistance	2	Litchman, Edelman, & Donaldson, 2018; Shaw & Johnson, 2011.	/	/
Sensitive issues	Contraception and sexual issue	2	/	Rasmussen et al., 2007; Wilson, 2013.	/

Results

Basic Characteristics of Included Articles

The included articles were published between 2005 and 2024. The number of publications increased notably over time, with the majority of articles published after 2011 (see Figure 2). This trend reflects the rapid development of mobile internet and social media, which have become increasingly important sources of health information. In addition, the articles originated from 14 countries, as determined by the affiliations of the first authors. The leading countries publishing related articles included the USA (n=12, 29.27%) (Connolly & Crosby, 2014; Eke et al., 2022; Greene et al., 2011; Hughes et al., 2021; Jackson et al., 2005; Litchman, Edelman, & Donaldson, 2018, Litchman, Rothwell, & Edelman, 2018; Longo et al., 2010; Shaw & Johnson, 2011; Stellefson et al., 2019; Tenderich et al., 2019; Zhang et al., 2018), the UK (n=8, 19.51%) (Allen-Taylor et al., 2022; Alzahrani et al., 2022; Da Moura Semedo et al., 2023; Fergie et al., 2016; Lewis et al., 2018; Mills et al., 2015; Pounds et al., 2018; Wilson, 2013) and Denmark (n=5, 12.20%) (Kjærulff et al., 2024; Kjærulff & Langstrup, 2023; Lindgreen et al., 2024; Newton et al., 2012; Vitger et al., 2017).

Figure 2.

Distribution of publication years

Demographic Characteristics and Research Methodologies of the Included Articles

The included studies spanned heterogeneous populations of people with diabetes. Specifically, 12 articles (29.27%) focused on people with T2DM (Allen-Taylor et al., 2022; Da Moura Semedo et al., 2023; Jackson et al., 2005; Jamal et al., 2015; Leelavathi et al., 2018; Lewis et al., 2018; Lindgreen et al., 2024; Mills et al., 2015; Newton et al., 2012; Vitger et al., 2017; Weymann et al., 2016; Wilson, 2013), while three (7.32%) examined populations with type 1 diabetes mellitus (T1DM) (Giménez-Pérez et al., 2016; Hughes et al., 2021; Yamamoto et al., 2011). Four (9.76%) studies included both people with T1DM and T2DM (Kalantzi et al., 2015; Kebede & Pischke, 2019; Kjærulff et al., 2024; Kjærulff & Langstrup, 2023). Two (4.88%) articles covered three types of diabetes (T1DM, T2DM and Latent Autoimmune Diabetes of Adulthood) (Litchman, Edelman, & Donaldson, 2018, Litchman, Rothwell, & Edelman, 2018). Additionally, 20 (48.78%) articles did not specify the diabetes type of the studied population (Abedin et al., 2017; Alotaibi & Mohd, 2021; Alzahrani et al., 2022; Connolly & Crosby, 2014; Eke et al., 2022; Fergie et al., 2016; Gabarron et al., 2020; Greene et al., 2011; Hyman et al., 2012; Longo et al., 2020; Lui et al., 2015; Meyfroidt et al., 2013; Pounds et al., 2018; Rasmussen et al., 2007; Shaw & Johnson, 2011; Stellefson et al., 2019; Tenderich et al., 2019; Yao et al., 2022; Zhang et al., 2018; Årsand et al., 2019).

As shown in the Table 1, of the 41 studies included in this review, they were categorized into three main groups based on their core methodological approaches and research foci: quantitative survey studies (n=15), qualitative studies (n=12), and social media content and network analyses (n=14). Quantitative survey studies formed a significant part of this study. Among these 15 studies, the majority (13 studies) employed a cross-sectional survey design (Alotaibi & Mohd, 2021; Eke et al., 2022; Giménez-Pérez et al., 2016; Hyman et al., 2012; Jackson et al., 2005; Jamal et al., 2015; Kalantzi et al., 2015; Kebede & Pischke, 2019; Leelavathi et al., 2018; Litchman, Edelman, & Donaldson, 2018; Shaw & Johnson, 2011; Vitger et al., 2017; Yamamoto et al., 2011), aiming to quantify the prevalence and sociodemographic determinants of OHIS behaviors among people with diabetes. These studies consistently identified age, education level, and income as key predictors of OHIS behavior, with significant variations observed across different racial groups and regions. In addition, this category included one longitudinal quantitative study (Lui et al., 2015), which utilized data from the Australian “Living with Diabetes Study” cohort to reveal dynamic associations between diabetes duration, neuropathy, glycemic control, and internet use. Furthermore, one randomized controlled trial (Mills et al., 2015) was included, which examined the effect of different delivery methods of genomic test results (online viewing vs. in-person genetic counseling) on participants' information-seeking and sharing behaviors, finding that participants who received results face-to-face were more likely to share their results with others.

Qualitative studies (n=12) provided in-depth insights into the motivations, experiences, and perceptions underlying people’s online behaviors. The majority of these studies employed purely qualitative designs, including semi-structured interviews (Fergie et al., 2016; Kjærulff & Langstrup, 2023; Litchman, Rothwell, & Edelman, 2018), Focus groups (Connolly & Crosby, 2014; Longo et al., 2010; Meyfroidt et al., 2013), grounded theory (Rasmussen et al., 2007), online observation (Lindgreen et al., 2024) and online ethnography (Kjærulff et al., 2024). These studies revealed the core logic of people using the internet as a supplementary information source: when faced with time constraints during clinical consultations or conflicting information, people turned to online communities for experiential knowledge and emotional support.

Social media content and network analyses (n=14) (Abedin et al., 2017; Allen-Taylor et al., 2022; Alzahrani et al., 2022; Da Moura Semedo et al., 2023; Gabarron et al., 2020; Greene et al., 2011; Lewis et al., 2018; Pounds et al., 2018; Stellefson et al., 2019; Tenderich et al., 2019; Weymann et al., 2016; Yao et al., 2022; Zhang et al., 2018; Årsand et al., 2019) constituted an important category in this review, primarily focusing on user-generated content and interaction dynamics on platforms such as Facebook, Twitter, Instagram, YouTube, and online forums. This category can be broadly divided into two subcategories. The first subcategory comprises content analysis studies (e.g., Abedin et al., 2017; Gabarron et al., 2020; Greene et al., 2011; Pounds et al., 2018; Tenderich et al., 2019), which employed quantitative or qualitative content analysis to code and quantify post types, information usefulness, or categories of social support. The second subcategory includes network analysis and cross-group comparison studies (e.g., Kjærulff et al., 2024; Stellefson et al., 2019; Yao et al., 2022; Årsand et al., 2019), which examined interaction patterns or compared discussion themes between different types of groups. Articles are focused on one of the following: (1) The characteristic of online health information (types and sources); (2) Factors influencing the OHIS behaviors of people with diabetes; (3) Incidence and barriers to OHIS behaviors of people with diabetes. A framework of OHIS behaviors among people with diabetes is displayed in Figure 3.

Figure 3.

Framework of OHIS behaviors among patients with diabetes

The Characteristic of Online Health Information

Information Types

The types of online health information were the most commonly reported aspects among these articles. The categorization of health information types was adapted from Kuske et al. (2017) and Zhao et al. (2022b), resulting in the identification of 13 types of online health information for people with diabetes (Table 2). Lifestyle management (53.66%), general diabetes information (41.46%), and treatment details (36.59%) were the most commonly reported topics. However, a notable discrepancy existed between information seeking and sharing. Yao et al. (2022) found that while self-management information accounted for 29.13% of information-sharing threads, it represented only 8.88% of information-seeking threads, suggesting that spontaneously shared content did not adequately address people’s specific self-management needs. Peer support was a prominent theme, mentioned in 46.34% of studies, whereas only 9.76% of studies (Alotaibi & Mohd, 2021; Kebede & Pischke, 2019; Litchman, Edelman, & Donaldson, 2018; Årsand et al., 2019) reported people with diabetes using online platforms to communicate with physicians. This finding indicates that digital patient-provider communication has received considerably less research attention compared with peer support. It is worthwhile to emphasize that individual articles may address more than one type of online health information. The articles described a range of sources for seeking online health information used by people with diabetes.

Information Sources

As shown in Table 3, people with diabetes utilized a diverse range of online sources to access health information. Social media and blogs were the most frequently reported sources, mentioned in 28 studies (68.29%). Among these, platforms such as Facebook, WeChat, and diabetes online communities were commonly used for peer interaction and social support, as they facilitate direct communication, group discussions, and the formation of ongoing social connections (Litchman, Edelman, & Donaldson, 2018; Rasmussen et al., 2007). These were followed by social media platforms with weaker personal connections, including Twitter, which was referenced less frequently but often served as a channel for disseminating health news and research updates (Gabarron et al., 2020). General internet sources representing all web-based platforms were described in 15 articles (36.59%). Health websites were referenced in five studies (12.20%), with ownership distributed across governmental organizations (e.g., American Diabetes Association), professional societies (e.g., Saudi Society of Endocrinology and Metabolism), and nonprofit institutions (e.g., Mayo Clinic). General search engines such as Google and Yahoo were mentioned in only three articles (7.32%). Furthermore, patient portals were addressed in a single study (2.44%).

Table 3.

Sources of Online Health Information Mentioned in the Articles (n=41)

Sources	Number of the articles (n)
Social media/blogs (eg. Facebook, Twitter, Wechat, DOC)	28 (Abedin et al., 2017; Allen-Taylor et al., 2022; Alotaibi & Mohd, 2021; Alzahrani et al., 2022; Da Moura Semedo et al., 2023; Eke et al., 2022; Fergie et al., 2016; Gabarron et al., 2020; Giménez-Pérez et al., 2016; Greene et al., 2011; Hughes et al., 2021; Jamal et al., 2015; Kebede & Pischke, 2019; Kjærulff et al., 2024; Kjærulff & Langstrup, 2023; Lewis et al., 2018; Lindgreen et al., 2024; Litchman, Edelman, & Donaldson, 2018, Litchman, Rothwell, & Edelman, 2018; Mills et al., 2015; Pounds et al., 2018; Shaw & Johnson, 2011; Stellefson et al., 2019; Tenderich et al., 2019; Vitger et al., 2017; Yao et al., 2022; Zhang et al., 2018; Årsand et al., 2019)
General internet	15 (Connolly & Crosby, 2014; Eke et al., 2022; Giménez-Pérez et al., 2016; Hyman et al., 2012; Jackson et al., 2005; Kalantzi et al., 2015; Leelavathi et al., 2018; Lui, et al., 2015; Meyfroidt et al., 2013; Mills et al., 2015; Newton et al., 2012; Rasmussen et al., 2007; Weymann et al., 2016; Wilson, 2013; Yamamoto et al., 2011)
Health websites (eg. WebMD, American Diabetes Association)	5 (Giménez-Pérez et al., 2016; Jamal et al., 2015; Longo et al., 2010; Mills et al., 2015; Vitger et al., 2017)
General search engines (eg. Google, Yahoo)	3 (Fergie et al., 2016; Jamal et al., 2015; Longo et al., 2010)
Patient portals	1 (Shaw & Johnson, 2011)

DOC = diabetes online communities.

Factors of OHIS Behaviors

11 articles (26.83%) reported factors that affect OHIS behaviors among people with diabetes. Younger, female gender, higher educational level, shorter duration of diabetes, longer duration of Internet usage, and higher household income were associated with a higher likelihood of seeking health information online (Alotaibi & Mohd, 2021; Eke et al., 2022; Giménez-Pérez et al., 2016; Jackson et al., 2005; Jamal et al., 2015; Kalantzi et al., 2015; Leelavathi et al., 2018; Lewis et al., 2018; Lui et al., 2015; Mills et al., 2015). Besides, other studies identified marital status (Eke et al., 2022; Jamal et al., 2015; Lui et al., 2015), occupation (Leelavathi et al., 2018), insurance type (Eke et al., 2022), HbA1c (Giménez-Pérez et al., 2016; Lui et al., 2015), race (Eke et al., 2022; Mills et al., 2015), people with diabetes activation (Eke et al., 2022; Lui et al., 2015), type of diabetes (Kalantzi et al., 2015; Litchman, Edelman, & Donaldson, 2018) and quality of care (Lui et al., 2015) as factors influencing OHIS behaviors in people with diabetes. Furthermore, several studies concluded that OHIS behaviors play an essential role in self-care activities such as blood glucose testing, hypoglycemia management, and non-pharmacological management among health related information users (Jamal et al., 2015; Kebede & Pischke, 2019).

Health Topics Sought by People With Diabetes

The type of information sought was influenced by different types of diabetes. People with T1DM demonstrate a greater tendency to seek information on intensive therapies, including insulin treatment, through social forums, and constitute a larger users base within diabetes online communities (DOCs) (Hughes et al., 2021; Litchman, Edelman, & Donaldson, 2018). In contrast, other online communities, such as PatientsLikeMe, attract a higher proportion of people with T2DM (Litchman, Edelman, & Donaldson, 2018). People with T2DM tend to focus more on obtaining advice related to diabetes management experiences and daily challenges, such as diet for diabetes and exercise (Jamal et al., 2015; Leelavathi et al., 2018). Age also affect OHIS behaviors between younger and older adults with T2DM across content, frequency, and channel preferences. In terms of search content, younger people demonstrate a stronger preference for emerging technologies such as continuous glucose monitoring, mobile applications, and lifestyle integration strategies, alongside a focus on psychosocial adjustment and peer-based experiential learning (Fergie et al., 2016). In contrast, older adults prioritize information related to medication adherence, complication management, and comorbidities, reflecting a more pragmatic and medically oriented approach (Lewis et al., 2018; Litchman, Rothwell, & Edelman, 2018). Regarding search frequency, younger adults engage more frequently and interactively, often utilizing multiple platforms for real-time updates and social validation (Fergie et al., 2016), while older adults exhibit more deliberate, episodic OHIS behaviors guided by specific health needs and greater trust in authoritative sources (Litchman, Rothwell, & Edelman, 2018). With respect to search channels, younger people with diabetes predominantly use social media, video platforms, and specialized health applications that support interactive and visual communication (Fergie et al., 2016). Conversely, older adults prefer structured, text-based formats found in dedicated online forums, email newsletters, and institutionally endorsed websites (Lewis et al., 2018; Litchman, Rothwell, & Edelman, 2018). In additional, engagement in DOCs significantly predicted HbA1c levels. Litchman, Edelman, and Donaldson (2018) found that each one-point increase in DOCs engagement was associated with a 33.8% decrease in the likelihood of having an HbA1c level ≥7%. Conversely, participants who did not engage in DOCs showed 2.7 times higher odds of having an HbA1c level ≥7%.

Incidence of OHIS Behaviors Among People With Diabetes

The incidence of OHIS behaviors among people with diabetes was recorded in 15 articles (36.59%). The most frequently documented data originated from the USA, wherein a significant increase from 40.00% in 2005 (Jackson et al., 2005) to 64.49% in 2022 (Eke et al., 2022) was observed. In Canada, the utilization rate of the Internet by people with diabetes was 28.80% in 2012 (Shaw & Johnson, 2011). Similarly, the use of the internet to seek health information in Europe significantly increased from 11.30% in 2013 (Kalantzi et al., 2015) to 75.00% in 2016 (Giménez-Pérez et al., 2016). Two studies reported that the usage rate of the internet for seeking health related information in Asian regions was 25.40% in Japan in 2011 (Yamamoto et al., 2011) and 71.60% in Saudi Arabia in 2015 (Jamal et al., 2015). And the prevalence of OHIS behaviors among people with diabetes was 92.71% in Australia in 2015 (Lui et al., 2015) and 38.40% in Malaysia in 2018 (Leelavathi et al., 2018). Overall, the prevalence of OHIS behaviors among people with diabetes has shown a progressive increase over time. Additionally, significant regional variations in these behaviors have been observed.

Barriers to OHIS Behaviors of People With Diabetes

Three types of barriers to OHIS behaviors among people with diabetes were identified: individual-related, equipment-related, and social barriers. Among these barriers, individual-related barriers were the most frequently reported, followed by equipment-related barriers, while social barriers were the least commonly mentioned. Among individual-related barriers, limited literacy, encompassing insufficient digital skills, difficulties in comprehending scientific terminology, and language barriers, was the most frequently reported obstacle (n=4, 9.76%) (Connolly & Crosby, 2014; Jackson et al., 2005; Kalantzi et al., 2015; Kebede & Pischke, 2019). Additional barriers identified included cost (n=3, 7.32%) (Connolly & Crosby, 2014; Jackson et al., 2005; Kalantzi et al., 2015) and lack of interest (n=1, 2.44%) (Jackson et al., 2005). Several disease-related factors were reported to impede OHIS behaviors, such as prolonged diabetes duration, family history of diabetes, and unemployment (Jamal et al., 2015). Furthermore, younger people with a higher educational level and shorter disease duration reported constraints (Connolly & Crosby, 2014; Kalantzi et al., 2015). At the same time, previous studies also evinced that psychological factors, such as anxiousness and fear, prevented effective OHIS behaviors, and that these barriers were more frequently reported in female participants (Jackson et al., 2005).

With regard to equipment-related barriers, four studies (9.76%) revealed that the volume of unorganized information can compromise disease management (Jamal et al., 2015; Kalantzi et al., 2015; Kebede & Pischke, 2019; Litchman, Rothwell, & Edelman, 2018). Moreover, two studies (4.88%) reported that some people with diabetes lack access to the appropriate technological infrastructure necessary for seeking health information online (Jackson et al., 2005; Kalantzi et al., 2015). Additionally, the Internet design (eg. layout and navigation modules, font design) and information characteristics (eg. credibility, usefulness, intelligibility, ease of use) represent key barriers (Connolly & Crosby, 2014; Kalantzi et al., 2015). In conclusion, multiple barriers hinder people with diabetes from engaging in online health information seeking.

Concerning social barriers, people residing in rural areas more frequently reported a lack of support from health care providers and challenges in managing the doctor-patient relationship (Kalantzi et al., 2015). Notably, Kjærulff and Langstrup (2023) found that neither clinicians nor people with diabetes actively initiated discussions about social media information during clinical consultations, which perpetuated a “parallel world” where online information remained disconnected from formal care. These barriers result in insufficient social and professional support, which hinders the adequate fulfillment of the information needs of individuals with diabetes.

Discussion

This scoping review aimed to map the existing literature on the OHIS behaviors of people with diabetes, focusing on their patterns digital platforms use and content preference. Current research in this population has primarily concentrated on behavioral characteristics, influencing factors, incidence and barriers.

This study found a global increase in OHIS behaviors among people with diabetes, with pronounced regional disparities. OHIS adoption is higher and has grown faster in developed nations, for example, rising from 40.00% in 2005 to 64.49% in 2022 in the US, and reaching 92.71% in Australia in 2015, compared to 38.40% in Malaysia (2018). These variations stem form infrastructural, socio-educational, political, and cultural factors. Developed countries generally possess more robust digital infrastructure, higher digital literacy, stronger political commitment to e-health policies (Chanie et al., 2025; Lepere et al., 2022; Woldie et al., 2018), and cultural norms favoring proactive information seeking. Conversely, developing regions face limited rural internet access, lower digital skills (particularly among older adults), insufficient policy support, and cultural preferences for interpersonal health sources (Park et al., 2025; Xu et al., 2025). Disparities also persist within developed countries across age, education, and income, highlighting the multidimensional digital divide. Future research should investigate targeted interventions, including technological adaptation, digital literacy training, and supportive policies, to promote equitable access to trustworthy online health information. Addressing these structural, social, and cultural determinants is essential for advancing health information equity.

People with diabetes tend to seek health information from social media and blogs. Despite demanding significant time, these interactive resources supplement offline social support by facilitating the exchange od diabetes0related information. This aligns with Mold et al. (2018), who reported that social media and online communities are popular among people with diabetes, serving as a vital peer support platforms beyond casual conversation or advertising. These platforms enable users to seek information, express emotions, learn from others, and receive emotional support (Chen et al., 2025). Although evidence-based health websites are reliable and secure (Oxman et al., 2021; Shen et al., 2018), this study found they remain underutilized. This may be due to their specialized, in-depth content, which often results in lower readability and accessibility for general audience (Chen et al., 2025). Like other health information seekers, people with diabetes incorporate contextual details into their inquiries to obtain more relevant responses (Ma et al., 2025; Park et al., 2020). Thus, the potential of social media for diabetes management remains underexplored. Future studies should integrate contextual individual factors into personalized recommendation systems, including tailored engines, intelligent Q&A interfaces, and video-based content. Embedding artificial intelligence-driven navigation tools, applying user-centered design, establishing content credibility mechanisms, and incorporating digital literacy training into patient education could enhance engagement and self-management. These digital approaches may complement conventional diabetes care. Meanwhile, website readability should be in depth, evidence-based information, especially on official diabetes platforms, and the quality of top-ranked search engine and social media results should be improved.

General search engines are commonly used as an important source of health information, reflecting their convenience and public acceptance among people with diabetes (Kyriacou & Sherratt, 2019). However, many people with diabetes may access incomplete, superficial, or outdated information lacking credible sources or official endorsement. Some attempt to verify websites reliability through cross-checking, but this practice increases the cognitive burden and lead to erroneous conclusions (Gray et al., 2005; Kyriacou & Sherratt, 2019). These findings suggest two interrelated issues. First, cross-check, though used by some, is cognitively demanding and error-prone, indicaring room for improving e-Health literacy. Second, reliance on such labor-intensive strategies points to a lack of effective, user-friendly tools for evaluating online health information quality and credibility.

People with diabetes most commonly seek information on lifestyle management, peer support, and diabetes knowledge (eg. diagnosis, causes), consistent with prior studies (Elnaggar et al., 2020; Litchman et al., 2019; Ma et al., 2025). However, information needs vary across populations. Older adults have more extensive needs and greater demand for social support (Zhao et al., 2022b), and represent a vulnerable group whose needs differ from younger populations (Sinha & Serin, 2024). Variations in OHIS behaviors across diabetes types also highlight the need for tailored support. Providers should adopt a segmented strategy: algorithm-driven recommendations for younger users (Stifjell et al., 2025), moderated peer support networks for older adults (Litchman, Rothwell, & Edelman, 2018), and condition-specific content aligned with patient priorities. Age-friendly and motivation-sensitive web designs could complement traditional approaches to better accommodate diverse groups. Clinicians’ attitudes toward online health information also significantly shape the OHIS behaviors of people with diabetes (Benetoli et al., 2018; Kjærulff & Langstrup, 2023). When clinicians treat social media as unrelated, people with diabetes are left to navigate online information alone and bear sole responsibility for initiating discussions. This dynamic can lead to frustration and reduced motivation for self-management. Consequently, clinicians have a critical opportunity to guide people with diabetes toward credible resources, promote critical evaluation of online content, and bridge the gap between formal clinical care and informal peer-supported health information.

Consistent with previous studies (Zhao & Zhang, 2017), this review identified three categories barriers to OHIS among people with diabetes: individual, equipment, and social. For instance, people with diabetes often encounter large amounts of online information but lack tools to access sources credibility and relevance, a barrier also reported among people with cancer (Ferraris et al., 2023). Developing reliable tools to evaluate health information quality is essential to assist people with diabetes identify credible content. Furthermore, involving family and friends in supporting OHIS behaviors and establishing online diabetes communities for emotional support and information exchange may be beneficial. Notably, few intervention programs have been implemented to improve OHIS behaviors among people with diabetes, indicating considerable potential for improvement.

The findings revealed knowledge gaps that point to critical future research directions. First, there is an uneven focus across population groups. Although socio-demographic factors predict OHIS, studies targeting underserved subgroups, such as individuals with low socioeconomic status, ethnic minorities, or rural residents, remain scarce, despite noted racial disparities (Eke et al., 2022). Second, cross-sectional designs dominate. While useful for quantifying prevalence and correlates, they cannot establish causality or capture how information seeking evolves with disease progression. Third, theoretical and conceptual depth is insufficient. Although some studies have used established frameworks, such as social support theory (Da Moura Semedo et al., 2023). most remain descriptive, focusing on what information people seek and which platforms they use. Few have systematically examined psychosocial mechanisms like trust formation, emotional regulation, or integration of online information into clinical decision-making. For instance, although cross-verification strategies to assess information accuracy has been documented (Kalantzi et al., 2015; Kyriacou & Sherratt, 2019), the cognitive and emotional processes underlying this behavior remain underexplored. This gap presented opportunities for future research to develop practical tools for evaluating online information and to adapt or extend theoretical models for diabetes self-management in the digital age.

Strengths and Limitations

This scoping review has several strengths. First, the inclusion of both quantitative and qualitative studies allowed for a holistic understanding of OHIS behaviors, capturing not only the prevalence and correlates of information seeking but also the contextual and emotional dimensions that shape online engagement of people with diabetes. Second, the review identified critical gaps in the existing studies, including the predominance of cross-sectional designs, the limited theoretical development, and the lack of standardized outcome measures, thereby offering clear directions for future research.

This scoping review has several limitations. First, its focus on adults with diabetes limits generalizability to other age groups and chronic conditions. Second, inconsistent reporting of OHIS behavioral dimensions across studies restricts systematic comparison. Additionally, few studies address age-specific needs and behaviors, particularly younger individuals’ with diabetes and the access and evaluation challenges faced by older adults. The exclusion of grey literature may have introduced selection bias, overemphasizing published findings while missing emerging trends or contextual adaptations. Future research should standardize reporting, examine behavioral differences across ages and contexts, use large-scale digital data to identify individuals at high risk of being misled by online health information, and develop linguistically tailored interventions to enhance health literacy and digital engagement.

Implications for Practice

This study offers practical guidance for nurses supporting people with diabetes during OHIS. As frontline providers with frequent and sustained patient contact, nurses are well-positioned to address OHIS behaviors in clinical practice. They can routinely assess the online information seeking practices during consultations, including information types sought, websites visited, and confusion arising from online content, and serve as trusted guides by directing people with diabetes to credible, evidence-based resources and teaching critical evaluation of online health information. Given their central role in diabetes self-management education, nurses can integrate OHIS-related discussions into existing programs, helping people with diabetes understand how to complement, not replace, professional medical advice with online information. By incorporating OHIS support into routine practice, nurses can bridge the gap between online information and formal diabetes care. Future research should develop targeted interventions to enhance the accuracy and readability of online health information, improve comprehension of medical content, and establish tailored digital information delivery systems for different phases of diabetes management, thereby empowering this population to retrieve and use high-quality information effectively.

Conclusion

This review synthesizes key themes in current research on OHIS behaviors among people with diabetes, identifying five primary dimension: types, sources, influencing factors, incidence, and barriers. People with diabetes seek diverse health information online, shaped by socio-demographic, diabetes-related, and motivational factors. Barriers arise from individual, equipment and social related factors. This review also indicates that OHIS behaviors in this population remain understudied. Additional studies should analyze the relationship between sociocultural environments, internet environments, and OHIS behaviors. As more people with diabetes rely on online health information, targeted interventions are increasingly needed to guide them in selecting suitable sources. Finally, the evolving doctor-patient relationship in the information age warrants further investigation.

Supplemental Material

Supplemental Material - Online Health Information Seeking Behaviors Among People With Diabetes: A Scoping Review

Supplemental Material for Online Health Information Seeking Behaviors Among People With Diabetes: A Scoping Review by Zhijia Shen, MS, RN, Zhijie Qian, MB, Dr, Peipei Xu, MS, RN, Jialin Chen, Limei Yin, MS, RN, Tingting Cai, PhD, RN, Changrong Yuan, PhD, RN, FAAN in Sage Open Nursing

Supplemental Material

Supplemental Material - Online Health Information Seeking Behaviors Among People With Diabetes: A Scoping Review

Footnotes

Acknowledgments

We would like to express our gratitude to teacher Quentin, at the Fudan University Library, who assisted us in the literature search.

ORCID iDs

Zhijia Shen

Tingting Cai

Ethical Considerations

No ethical approval is required for this scoping review, and its protocol has been registered on the Open Science Framework (OSF) platform, available at .

Author Contributions

Zhijia Shen., Tingting Cai. and Changrong Yuan. contributed to the study conceptualization and design. Zhijia Shen., Zhijie Qian., Jialin Chen. and Peipei Xu. participated in the analysis and interpretation of the data. Zhijia Shen. prepared the first draft of the manuscript. Limei Yin., Tingting Cai. and Changrong Yuan. supervised the process, reviewed and commented comprehensively on the manuscript. All authors have revised the paper critically for important intellectual content and approved the final version to be submitted.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China [72374048 to Changrong Yuan]; the Jiangsu State Administration of Traditional Chinese Medicine [MS2024084 to Limei Yin]; the Jiangsu Association of Chinese Medicine [ZXFZ2024089 to Zhijia Shen]; and the SuZhou Municipal Health Commission [KJXW2023070 to Zhijia Shen].

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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