Abstract
Purpose
The purpose of this study was to develop a model of skilful surfing to aid understanding of how best to seek health information, online and offline, during pregnancy.
Design/methodology/approach
This study used an observational, descriptive design, using a purpose written questionnaire, factor analysis and structural equation modelling.
Findings
Analysis resulted in the factor loading of five components: online health information seeking behaviour, normalisation, offline support, trust and data saturation. These components were included as latent variables in an SEM to evaluate the credibility, and subsequently confirm the viability of the theory of skilful surfing.
Originality/value
To the authors' knowledge, this study is the first of its kind to successfully model and define what it is to skilfully surf health information online whilst pregnant, with empirical and theoretical underpinnings.
Keywords
Citation
Rathbone, A.L., Clarry, L. and Prescott, J. (2024), "Skilful surfing: modelling the optimum method of online health information seeking during pregnancy", Mental Health and Digital Technologies, Vol. 1 No. 1, pp. 53-72. https://doi.org/10.1108/MHDT-12-2023-0005
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
Introduction
Health information seeking behaviour
Health information seeking behaviour (HISB) is multifaceted and subjective (Zimmerman and Shaw, 2020; Luo et al., 2022; Kodithuwakku and Gunarathna, 2023), and not a concept that can be defined succinctly. It has been conceptualised in various ways, yet a standardised characterisation is yet to be found.
Lenz (1984) concurred that HISB is a complex theory, defining the act as a “-series of interrelated behaviors that can vary along two main dimensions; (a) extent and (b) method” (p. 63). Baker and Connor (1994) explored how HISB is portrayed, stating, “-any activity undertaken to satisfy a query can be designated as information-seeking behaviour”. Rees and Bath (2000) described HISB as “-a problem-focused coping strategy sometimes adopted by individuals as a response to a threatening situation” (p. 72). Rees and Bath (2001) furthered that HISB was a form of self-monitoring, calling HISB “-the urge to confront oneself with the threatening situation by means of seeking more information about it” (p. 900). Czaja et al. (2003) defined HISB as the number of sources an individual used for data acquisition, whilst Lambert and Loiselle (2007) stated that; “HISB is characterized by the type and amount of health-related information sought, the specific actions implemented to obtain the information, and the sources individuals use” (p. 1013).
Models of health information seeking behaviour
The health information acquisition model (HIAM; Freimuth et al., 1989) incorporated aspects of broader decision-making models (Bettman, 1979) and focused largely on the information acquisition process. Heavily influenced by Lenz (1984), the HIAM consists of six steps: stimulus (initial query), information goals, cost/benefit analysis of searching, search behaviour, evaluation of information and decision point on adequacy of information.
The comprehensive model of information seeking (CMIS; Johnson et al., 1995) consists of three primary variables; antecedents (the reason for information seeking), information carrier characteristics (that influence how and where one acquires data) and information seeking actions (the nature of the search and the subsequent outcomes).
The planned risk information seeking model (PRISM; Kahlor, 2010) is an adapted version of the risk information seeking and processing model (RISP; Griffin et al., 1999). The model depicts new relationships, reflecting aspects of the theory of planned behaviour (TPB; Ajzen, 1991) and the HIAM (Freimuth et al., 1989), amongst others. The PRISM identifies predictors of information seeking behaviour (Hovick et al., 2014). The model suggests that an individual’s intention to information seek is dependent on awareness of knowledge insufficiency, how they perceive risks and responses and beliefs towards health information seeking.
Online health information seeking behaviour
Although the internet cannot be deemed a principal proxy for conventional care, it has become a fundamental alternative (Zhao et al., 2022; Tang et al., 2023). Surveys have shown that 54%–88% of people have used the internet to research symptoms, enquire about medication and side effects, research current health conditions, whilst remaining anonymous (Fox, 2011; Fox and Duggan, 2013, Sillence et al., 2007).
Online HISB (oHISB) has many positive benefits, such as encouraging active participation in health care, increasing health literacy and providing reassurance (Link et al., 2021; Hassan and Masoud, 2021). Gray et al. (2005) evidenced that the internet defies the barriers of data acquisition, facilitating access to a surfeit of health information. They suggested that people found the online platform more appealing as it enables optional anonymity.
The internet allows users to make informed decision regarding health-care engagement (Luo et al., 2022), possibly reducing waiting times and unnecessary GP appointments, subsequently relieving strain on the health-care sector. The online platform provides psychoeducation and further understanding via credible sources (Soroya et al., 2021; Nutbeam and Lloyd, 2021), social media platforms (Basch et al., 2022) and counselling (Smith et al., 2022), amongst others.
Searching online for health information is not without pejorative consequences. Not all health information online derives from credible sources (England and Harriss, 2016). If an individual cannot effectively navigate online information, invalidated health information and personal experiences can be perceived as true (Irizarry, 2017; Battineni et al., 2020). Personal accounts of health issues from others may cause users to misconstrue their symptoms and assume that they are experiencing the worst-case scenario (Sillence, 2017). Whilst oHISB on social media platforms can reduce stigma, people may inadvertently find their anonymity compromised (Best, Gil-Rodriguez, Manktelow and Taylor, 2016).
Pregnancy and online health information seeking behaviour
On average, one in five clinical pregnancies are not viable in the first trimester (Garcıa-Enguıdanos et al., 2002; Savitz et al., 2002). The internet can help pregnant women gain advice about nutrition, exercise and precautions to take, weeks prior to being admitted under prenatal care (Zhu et al., 2019; Ghazi, 2021). Positive oHISB can aid understanding of health information given by professionals during or post-antenatal appointments (Song et al., 2012; Vamos et al., 2019; Thapa et al., 2021).
Nulliparous women may become increasingly concerned when experiencing symptoms typical to pregnancy. Multiparous women may become concerned if symptoms differ to previous pregnancy(ies). Due to the negative consequences oHISB can trigger, it is vital that pregnant women are equipped with the aptitude to skilfully search online for health information. Skilful surfing is a complex matter as there are several attributes to the overall dexterity of the term.
Key studies: pregnancy and online health information seeking behaviour
For the purpose of this research, themes were extracted from three key studies.
One mixed method study explored what triggers pregnant women to partake in oHISB and the subsequent effects (Prescott and Mackie, 2017). This study found that the amount of time spent searching online influenced the ability to seek information online effectively. Some found it hard to know when to stop, triggering extensive and unnecessary searches. Participants tended to repeat searches that had been carried out earlier by using various sources, seeking consistency and readability, assurance and disagreement with information obtained. Search repetition and the inability to identify reliable sources increased time spent online and pregnancy specific health anxiety (HA; Rathbone and Prescott, 2019). Data saturation promoted reassurance via further understanding and exhausting all sources. Most participants were able to decipher source credibility, with many opting to use NHS/government endorsed websites. However, this is not true for the entirety of internet users. Online health information can be difficult to navigate due to lack of ethical or governmental regulatory bodies managing uploaded information (Eastin, 2001). This may cause uncertainty when attempting to determine source credibility (Adoni, Cohen and Mane, 1984). Reading similar posts and blogs reduced worry, ensured the participant that many of their symptoms were typical, normalised and promoted a sense of belonging and familiarisation. Frost and Massagli (2008) found that when patients shared similar information online, more positive social evaluations were made, and social bonding was instigated. However, there remains the risk of misconstruing subjective narratives. Prescott and Mackie (2017) reported that “worst case scenarios” could trigger, or exacerbate HA specific to pregnancy for some users, as their own symptoms were misinterpreted, and the worst outcomes were feared/expected.
One quantitative study (Prescott et al., 2018) investigated predictors of HA during pregnancy. The authors reported that, when pregnant, if women are not able to identify that they have sought enough information to satisfy their query, HA increased, alongside repeated searches. It is possible that this was due to users being faced with a mass of conflicting information. Conversely, the use of multiple sources was not a significant predictor. This may be due to participants accessing multiple sources to achieve the highest level of credible information (Rathbone et al., 2022).
In a third study (Rathbone and Prescott, 2019), authors suggested that repeated online searches for the self did not significantly predict HA during pregnancy, yet searching for the unborn child was. This may indicate that pregnant women experiencing HA may carry their anxieties into motherhood postpartum. These key studies evidence the need for a resource to support pregnant women to engage in optimal oHISB.
Conceptual framework
Aspects of the above HISB models were sampled to inform the development of a purpose written questionnaire and subsequently, the skilful surfing model. Themes from the key studies strongly relate to the hypothesised model. For example, within the HIAM, the cost/benefit analysis of searching related to the possibility of increased HA. In addition, the evaluation of the information decision point on adequacy of information, was akin to the theme of data saturation. Within the CMIS, the antecedents aspect explains who an individual is searching for (mother or child). The information carrier characteristics relate to the reliability of information. PRISM was most similar to themes within the key studies. The PRISM recognises the affect risk response (increased HA) due to a health issue, lack of knowledge and subsequent searching. The seeking subjective norms aspects is akin to the theme of normalisation and the perceived information seeking control aspect encompasses what is to be defined in this study as “Skilful Surfing”.
The model aspects and key studies informed the development of the purpose written questionnaire, alongside the sampling of items from e-Health Impact Questionnaire (e-HIQ; Kelly Ziebland and Jenkinson, 2015; Figure 1).
Rationale and aims
A resource to support pregnant women navigating online health information effectively is necessary. The aim of this study was to develop a model of skilful surfing.
Method
Design
This study used a mixed method design. Models, key studies and samples from a validated measure were used to inform the development of a purpose written questionnaire and underpin the theoretical modelling of skilful surfing (Figure 1).
Materials
The Pregnancy-related Skilful Surfing Questionnaire (P-SSQ) was a purpose written, multiple-choice, 26-item scale. The initial four questions related to general and pregnancy demographics. The following 14 items were informed by aspects of aforementioned models and themes from the key studies. The final eight items were sampled from the e-HIQ (Kelly Ziebland and Jenkinson, 2015). This questionnaire is used to compare the potential encounters and consequences experienced when using websites containing health-related information. The e-HIQ is dually fragmented. The e-HIQ 1 explores general views about the use of online health information websites. The e-HIQ 2 consists of questions that relate to any one specific website. For this study, only the e-HIQ 1 was relevant. The e-HIQ 1 consists of two sub-scales: attitudes towards online health information and attitudes towards sharing health experiences online. Both sub-scales presented an internal consistency of ≥ 0.77.
Procedure
Participants were recruited through social media and parenting chatrooms. All social media and chatrooms were public, therefore; no prior permission was required to post study details. The National Childbirth Trust (NCT) also promoted the study. The link directed participants to the Qualtrics platform, where they were presented with a participant information sheet (PIS), consent form, signposting where necessary and the P-SSQ.
Participants
The initial sample consisted of 301 pregnant women, at various trimesters of their pregnancy, both primigravida and multigravida.
Data handling and analysis
Data were analysed using SPSS version 25 and Amos 25 Graphics. Missing data was supplemented with person mean substitution (Downey and King, 1998). Participant 97 was removed due to more than 20% missing data. Data was tested for normality. Some responses were mildly skewed, which was expected due to the nature of the statements and the overall study.
Descriptive statistics of the remaining 300 participants were explored. Data were then separated to complete exploratory and confirmatory factor analysis (CFA), respectively. Data from 110 participants were factor analysed, and data from 190 participants were included in a structural equation model (SEM). Previous research reports that five participants per variable (questionnaire item) in exploratory factor analyses (Floyd and Widaman, 1995) and 100–200 participants overall in SEMs (Loehlin, 2004; Hoyle, 1995) ensure high-quality data analysis.
Ethical considerations
When obtaining consent from participants, ethical issues were taken into consideration, considering the subjective physical and psychological experiences, both positive and negative. Participants were informed that their involvement was voluntary, and that they could withdraw at any time, without risk of negative consequences. Participants were advised that their data would be anonymous and informed of their rights under General Data Protection Regulation (GDPR). In general, participants were offered initial signposting to the Samaritans charity.
To mitigate the possible ramifications, participants were given the email of the lead researcher to ask further questions. If participants were to disclose that they had experienced (or were currently experiencing) specific issues relating to pregnancy [e.g. hyperemesis gravidarum (HG), miscarriage, psychosis, stillbirth, etc.), the lead researcher had collated a comprehensive list of explicit charities and organisations equipped to support said women with issues (e.g. Pregnancy Sickness Support, Tommy’s, Mind, etc.). Throughout the course of the study, no participants requested further signposting.
Results
Descriptive statistics
Participants were aged 16–20 (7.7%, n = 23), 21–25 (26%, n = 78), 26–30 (30.3%, n = 91) and 31–35 (26%, n = 78), 36–40 (8.7%, n = 26) and over 41 (1.3%, n = 4). Regarding trimester, 19.3% were in the initial trimester (n = 58), 33.7% were in the second (n = 101) and 47% (n = 141) were in the third. Of the sample, 38% (n = 114) were pregnant with their first child and 62% (n = 186) with their second. Only 3.7% of the participants received medical help when attempting to conceive (n = 11), the majority conceived naturally (96.3%, n = 289).
Factor analysis
Reliability analysis
The Kieser–Meyer–Olkin test for sampling adequacy evidenced adequate sampling (0.753). The Bartlett test for sphericity also indicated significance (0.000). Thus, the data was deemed suitable for factor analysis. Item-total correlations were mostly significant.
Factor analysis
A principal component analysis (PCA) was conducted using the varimax rotation. Kaiser’s normalisation criterion resulted in the observed six factor solution, which explained 64.869% of the variance, which was deemed acceptable for the solution (Hair, Black, Babin, Anderson and Tatham, 1998). Table 1 displays the primary eigenvalues and percentage of variance explained by each of the emergent components.
Within the six-factor solution, all components were well represented with communalities spanning from 0.469 to 0.793. Only two items (I search online for health information for my child/children and The internet is a reliable resource to help me understand what a doctor tells me) were pure factor loading. For items that loaded onto more than one factor on the component matrix, higher item loading was accepted for each factor. Components were finalised and reported, supported by statistical analysis, as opposed to theoretical underpinnings. Item loadings are reported in Table 2.
Overview of factor analysis
To appropriately analyse the inclusive 22 items within the P-SSQ (minus the initial four general and pregnancy demographic questions), a PCA was executed. A structure of six orthogonal components were identified; data saturation, searching online, oHISB, normalisation, offline support and trust (Table 3).
Using these six observed factors, further analysis was conducted by way of a SEM.
Structural equation model
Authors removed Component 2. Component 2 confirmed engagement in searching online and did not offer behavioural insight. Figure 2 displays the hypothesised model of skilful surfing.
The latent variables of the model were oHISB, data saturation, trust, normalisation and offline support (Appendix 1).
Model 1
Model 1 did not quite fit the data. Whilst the root mean square error of approximation (RMSEA) of the model did not exceed 0.08, indicating an acceptable error of approximation, the Tucker–Lewis Index (TLI) and the comparative fit index (CFI) fell below a figure denoting good model fit. Appendix 2 presents Model 1, its fit indexes, regression weights and covariances.
It was considered that a skewness of data could be a contributing factor affecting the goodness of fit. However, this data set was only mildly skewed (Bentler and Chou, 1987; Malthouse, 2001), therefore did not violate this assumption. Neither data sample size inadequacy nor missing data was an influential factor for goodness of fit.
Within Model 1, one of the regression coefficients was non-significant; oHISB and Q15. This item was “I check the reliability of online health information sources”. This was unexpected as checking the reliability of sources was considered a vital to skilful surfing, to ensure that information obtained was credible. It was considered that this may account for subjective levels of trust in sources, regardless of reliability.
Whilst it is acceptable to modify SEMs based on empirical data, the more empirically based modifications integrated into the model, the less likely it is that the model will be replicable with new data sets (Arbuckle and Wothke, 2004). It is preferred that SEMs are altered based on theory. When considering model modification, theoretical underpinning of the items were taken into consideration, as opposed to Model 1 statistics.
Model 2
After careful deliberation, four items were removed from the model; “I repeat an online health search” (Q8), “I am satisfied I have read enough online health information to answer my questions” (Q10), “The internet is a reliable resource to help me understand what a doctor tells me” (Q24) and “Sometimes I feel embarrassed by my pregnancy related symptoms” (Q26).
Q8 was removed due to dual implication. Responding “never” to this statement could suggest that the participant is not capable of skilfully surfing as it could be implied that they accept the first data source returned when searching. However, the same could be implied if the participant responded “always”, suggesting that they excessively search for health information.
Q10 was removed for the same reason. Responding “never” to this statement could imply that the participant does not know when to stop searching or their anxieties are not readily allayed. Responding “always” to this statement could suggest that the participant may be reading invalidated information, accepting it as correct and being satisfied with possibly inaccurate health information and advice.
Overall, the first two items were removed from the model as the statements hold no obvious direction in relation to the ability to skilfully surf and are both open to interpretation and researcher bias.
Q24 was removed due to the possibility of confounding, subjective variables skewing the data. For instance, with this statement the response will be heavily dependent upon the quality of the patient/doctor relationship. Furthermore, if the patient has high levels of HA, their opinion regarding the information they receive from a medical health professional may be prejudiced from the outset. Q26 was removed as it was deemed irrelevant in relation to skilful surfing. This was due to the ubiquity of pregnancy as a reproductive experience and the anonymity that the online platform provides. After removing these four items due to theoretical underpinning, Model 2 provided an overall good model fit (Figure 3).
The accepted model
The accepted model contained 41 variables, 15 of which were observed and 26 unobserved, 23 exogenous and 18 endogenous. The observed, endogenous variables were Q9, Q11, Q12, Q13, Q14, Q15, Q16, Q17, Q18, Q19, Q20, Q21, Q22, Q23 and Q25. Unobserved, endogenous variables were offline platform, online platform and skilful surfing. Unobserved, exogenous variables were, Online_HISB, data saturation, trust, norm, offline support, e2, e5, e6, e8, e9, e10, e11, e12, e13, e14, e15, e16, e17, e18, e19, res1, res2 and res3.
Table 4 shows that df = 83, χ2 = 132.588, GFI = 0.918, TLI = 0.906 and the CFI = 0.925, indicating an overall, improved fit in the model. RMSEA = 0.56 indicated acceptable error of approximation. Model 2 was accepted. Research suggests sample means, correlation matrices and covariance matrices should be reported for SEM analysis (McDonald and Ho, 2002; Table 4).
The following is the final accepted version of the skilful surfing model (Figure 4).
Discussion
The model of skilful surfing evidenced that oHISB has a direct impact on the online platform. This was expected as pregnant women who engage in oHISB could not do so without the internet.
Data saturation had a direct impact on the online platform. The internet facilitates access to a vast amount of information and unrestricted searches. Data saturation pertains to the perception of a satisfactorily answered question. If an enquiry is remedied, and data saturation achieved, searching will cease due to attaining a sense of reassurance, or seeking offline corroboration. To skilfully surf, pregnant women must be self-aware enough to know data saturation is achieved and when to cease searching. As acuity of data saturation is subjective, a lack of self-awareness may lead to extensive searching and increased HA. HA may be instigated by online information, yet continue offline, resulting in an increased burden on health-care professionals, resources and settings due to excessive use. Excessive use may be mediated using psychoeducation. This may enable women to better recognise increased HA and partake in self-guided care.
Normalisation had a direct impact on the online platform. As gravidity is a subjective experience, information and advice from those who have experienced similar symptomology can be reassuring and help to normalise symptoms. The normalisation aspect of the model of skilful surfing can help those who are pregnant to worry less about their symptoms once they ascertain that they are typical to gravidity. Whilst this applies to nulliparous women who may lack the familiarity and comprehension, which multiparous women have, it is also true for multiparous women in the sense that every pregnancy differs from the last.
Trust had a direct impact on the offline platform. For some women, the online platform alone did not provide sufficient information. Online health information could be miscomprehended, misrepresentative or even inaccurate. If pregnant women do not have the ability to effectively decipher the credibility of online sources, this may foster mistrust. Due to this, pregnant women may prefer to verbalise, clarify and allay their concerns offline, with the relevant health-care professionals. This may be more trusted due to the professionals’ experience, qualifications and governance by regulatory bodies.
Offline support had a direct impact on the offline platform. If pregnancy-specific HA cannot be allayed by health information seeking online, women may seek comfort, reassurance and support from others, such as family and friends. This network can provide a personal and informal manner of support. Familial and spousal relationships may be extremely influential for a pregnant woman and are encouraged within the model of skilful surfing. However, this aspect is only relevant to those who have an offline support network. Those without offline support networks face greater obstacles in obtaining personal offline support, therefore should be directed to relevant professionals and/or organisations that can provide this.
The online and offline platform had a direct impact on skilful surfing. The online platform cannot be used as a principal proxy for the offline platform. Whilst the online platform allows pregnant women to gain education, understand experiences and connect with others, the ability to skilfully surf will not be achieved if offline support is not accessed.
The sole use of the online platform may increase levels of pregnancy-specific HA due to: invalidated information, misunderstanding, attributing worst-case scenario experiences of others to their own symptomology and excessively searching. The sole use of the offline platform may increase pregnancy-specific HA due to lack of appointment time (availability and length). Women may leave appointments with unanswered questions. Therefore, the online platform may become a beneficial tool to acquire health information. The model of skilful surfing advises that both platforms be used simultaneously, yet appropriately, integrating both to alleviate pregnancy-specific HA and promote optimal mental health and well-being during pregnancy.
Future research
Previous research has highlighted the credibility of online resources as a salient concern. The integrity and reliability of online information may be called into question as there are no comprehensive criterion or stringent guidelines in situ to prevent the dissemination of misinformation. Digital information is easily manipulated, altered, plagiarised or misrepresented (Metzger, 2007). Due to the lack of regulations concerning information upload, almost anybody could assume the role of an author, yet lack the apt expertise to topically narrate, essentially providing only subjective opinions. This may be a pertinent concern during pregnancy when considering the multifaceted, subjective experience.
Whilst misinformation online may be prevalent, it is imperative that all women are adept in deciphering credibility. Previous research suggests that underserved demographics are more susceptible to unsubstantiated health information, such as those from BAME communities and older adults (Seo et al., 2021). Seo et al. (2021) posit that education is a significant factor associated with perception and understand of content and credibility. Other contributing factors effecting the ability to engage in skilful surfing include the capacity to access (digital exclusion; Doukani, 2021) and navigate (digital health literacy; Dunn and Hazzard, 2019; Van Hauwaert et al., 2024) the online platform.
It is imperative that future research within this field facilitates the inclusion of women from all cultures and socioeconomic status to ensure that the results are generalisable to a diverse population, inclusive of those from underserved communities.
Conclusion
To skilfully surf when pregnant, a woman must proactively engage in oHISB, have a sense of self-awareness when attaining data saturation, understand that most of their symptoms are typical to pregnancy and engage in research and conversation with others to obtain an adequate level of reassurance of this, effectively navigate online health information identifying credible sources, yet still engage with offline support networks and understand that both online and offline support are fundamental during pregnancy.
Figures
Figure 1.
Conceptual framework
Figure 2.
Hypothesised model of skilful surfing
Figure 3.
Model 2 – a model of skilful surfing
Figure 4.
A Final model of skilful surfing
Figure A1.
Model 1 and estimates
Initial eigenvalues and variance percentage
| Component | Eigenvalue | % of variance explained | Cumulative % |
|---|---|---|---|
| 1 | 5.893 | 26.786 | 26.786 |
| 2 | 2.753 | 12.516 | 39.302 |
| 3 | 1.754 | 7.971 | 47.273 |
| 4 | 1.548 | 7.035 | 54.307 |
| 5 | 1.231 | 5.595 | 59.902 |
| 6 | 1.0693 | 4.967 | 64.869 |
Source: Authors’ own creation
Components
| Item | Loading | |
|---|---|---|
| Component 1: data saturation | ||
| Q12 | I feel as though I can never have too much information on a health issue I am experiencing or have experienced* | 0.793 |
| Q11 | I continue to search for online health information after my health query has been answered* | 0.758 |
| Q9 | I repeat an online health search using a different source (e.g. blogs, social media, government health sites) | −0.710 |
| Q8 | I repeat an online health search | 0.672 |
| Component 2: searching online | ||
| Q5 | I search online for answers to health-related questions | 0.823 |
| Q6 | I search online for health information for myself | 0.796 |
| Q7 | I search online for health information for my child/children | 0.676 |
| Component 3: oHISB | ||
| Q15 | I check the reliability of online health information sources | 0.784 |
| Q10 | I am satisfied I have read enough online health information to answer my questions | 0.633 |
| Q13 | My anxiety/worry is eased by online symptom searches | 0.628 |
| Q17 | I feel reassured knowing that health symptoms I experience are typical to pregnancy | 0.574 |
| Component 4: normalisation | ||
| Q20 | Looking at health websites reassure me that I am not alone with my health concerns | 0.834 |
| Q18 | The internet is a good way of finding other people who are experiencing similar health problems | 0.760 |
| Q19 | It can be helpful to see other people’s health-related experiences on the internet | 0.752 |
| Component 5: offline support | ||
| Q22 | The internet can be useful to help people decide if their symptoms are important enough to go see a doctor | 0.748 |
| Q21 | I would use the internet if I needed help to make a decision about my health (e.g. whether I should see a doctor, take medication or seek other types of treatment) | 0.741 |
| Q25 | The internet is useful if you do not want to tell people around you (e.g. your family or people at work) how you feel | 0.596 |
| Q26 | Sometimes I feel embarrassed by my pregnancy-related symptoms | 0.595 |
| Component 6: trust | ||
| Q23 | I would use the internet to check that the doctor is giving me appropriate advice | 0.742 |
| Q14 | I trust all online health information* | −0.716 |
| Q24 | The internet is a reliable resource to help me understand what a doctor tells me | 0.630 |
| Q16 | I trust other people’s personal accounts of health issues (e.g. blogs, vlogs, personal social media)* | −0.459 |
Source: Authors’ own creation
Component contents
| Component no. | Title | Content |
|---|---|---|
| 1 | Data saturation | Four items concerning the amount of information searched for and whether women know when their query is sufficiently answered. Items within this factor relate to repetitive searches for health information. This may involve the use of different sources, a constant need for further education or even the continuation of data acquisition, following sufficient answers |
| 2 | Searching online | Three items relating to the search and the addressee. Whilst this component may seem similar to the third, it was termed searching online as the items depict the action of searching, as opposed to the behaviour |
| 3 | oHISB | Four items relating to the methods used to search for information. Behaviours inclusive in these items are validating sources, knowing when to cease searching, relieving anxiety and gaining reassurance regarding the symptom typicality |
| 4 | Normalisation | Three items pertaining to pregnant women understanding that their health symptoms are typical to pregnancy and that other women have had similar experiences. This type of reassurance helps women feel less alone, reassured, and abates unnecessary anxieties |
| 5 | Offline support | Four items, assessing how women use the internet to inform their offline health-care experience, attitudes and decision-making. The items in this factor evidence the ability to engage in one’s own health care, using the internet to make informed choices offline. It also highlights how the internet facilitates anonymity when engaging in online health information seeking |
| 6 | Trust | Four items concerning how trustworthy online information is, regardless of the source. It also suggests that the internet can be a valuable resource to confirm health information given offline, and vice-versa |
Source: Authors’ own creation
Accepted model (Model 2) estimates
| Fit indexes | |||||
|---|---|---|---|---|---|
| Df | x² | GFI | TLI | CFI | RMSEA |
| 83 | 132.588 | 0.918 | 0.906 | 0.925 | 0.056 |
| Sample means and standard deviations | ||
| Mean | SD | |
| Q9 | 2.49 | 1.259 |
| Q11 | 2.69 | 1.110 |
| Q12 | 3.53 | 1.271 |
| Q13 | 3.28 | 0.988 |
| Q14 | 2.43 | 1.035 |
| Q15 | 2.02 | 1.115 |
| Q16 | 3.19 | 1.052 |
| Q17 | 1.84 | 0.840 |
| Q18 | 1.73 | 0.840 |
| Q19 | 1.79 | 0.820 |
| Q20 | 1.61 | 0.695 |
| Q21 | 2.31 | 1.085 |
| Q22 | 2.16 | 0.929 |
| Q23 | 2.95 | 1.194 |
| Q25 | 1.89 | 0.943 |
| DataSat (data saturation total score) | 12.1895 | 2.39839 |
| OHISB (oHISB total score) | 10.0474 | 2.34416 |
| Normalisation (normalisation total score) | 5.1368 | 2.05795 |
| OfflineSupport (offline support total score) | 9.0105 | 2.85448 |
| Trust (trust total score) | 10.7579 | 2.11928 |
| Regression weights | ||||
| Estimate | S.E. | C.R. | P | Label |
| Online_platform ← Norm | 1.000 | |||
| Offline_platform ← Trust | 1.000 | |||
| Online_platform ← Data_saturation | 1.000 | |||
| Online_platform ← Online_HISB | 1.000 | |||
| Offline_platform ← Offline_support | 1.000 | |||
| Q13 ← Online_HISB | 1.000 | |||
| Q17 ← Online_HISB | 1.588 | 0.432 | 3.673 | *** |
| Q9 ← Data_saturation | 1.000 | |||
| Q11 ← Data_saturation | −2.494 | 0.820 | −3.041 | 0.002 |
| Q12 ← Data_saturation | −1.964 | 0.502 | −3.916 | *** |
| Q14 ← Trust | 1.000 | |||
| Q16 ← Trust | 1.484 | 0.354 | 4.191 | *** |
| Q23 ← Trust | −0.476 | 0.209 | −2.276 | 0.023 |
| Q18 ← Norm | 1.000 | |||
| Q19 ← Norm | 0.970 | 0.078 | 12.363 | *** |
| Q20 ← Norm | 0.709 | 0.067 | 10.569 | *** |
| Q21 ← Offline_support | 1.000 | |||
| Q22 ← Offline_support | 0.851 | 0.110 | 7.726 | *** |
| Q25 ← Offline_support | 0.330 | 0.086 | 3.815 | *** |
| Skilful_surfing ← Online_platform | 1.000 | |||
| Skilful_surfing ← Offline_platform | 1.000 | |||
| Q15 ← Online_HISB | 0.302 | 0.284 | 1.063 | 0.288 |
| Standardised regression weights | |
| Estimate | |
| Online_platform ← Norm | 0.485 |
| Offline_platform ← Trust | 0.420 |
| Online_platform ← Data_saturation | 0.277 |
| Online_platform ← Online_HISB | 0.240 |
| Offline_platform ← Offline_support | 0.698 |
| Q13 ← Online_HISB | 0.355 |
| Q17 ← Online_HISB | 0.661 |
| Q9 ← Data_saturation | 0.321 |
| Q11 ← Data_saturation | −0.910 |
| Q12 ← Data_saturation | −0.625 |
| Q14 ← Trust | 0.514 |
| Q16 ← Trust | 0.750 |
| Q23 ← Trust | −0.212 |
| Q18 ← Norm | 0.845 |
| Q19 ←Norm | 0.839 |
| Q20 ← Norm | 0.724 |
| Q21 ←Offline_support | 0.815 |
| Q22 ← Offline_support | 0.809 |
| Q25 ← Offline_support | 0.309 |
| Skilful_surfing ←Online_platform | 0.652 |
| Skilful_surfing ← Offline_platform | 0.564 |
| Q15 ← Online_HISB | 0.095 |
| Covariances | ||||
| Estimate | S.E. | C.R. | P | Label |
| Trust ↔ Offline_support | −0.232 | 0.068 | −3.403 | *** |
| Online_HISB ↔ Offline_support | 0.101 | 0.044 | 2.308 | 0.021 |
| Norm ↔ Offline_support | 0.315 | 0.064 | 4.913 | *** |
| Trust ↔ Norm | −0.191 | 0.054 | −3.555 | *** |
| Online_HISB ↔ Trust | −0.080 | 0.034 | −2.317 | 0.020 |
| Online_HISB ↔ Data_saturation | −0.026 | 0.017 | −1.535 | 0.125 |
| Online_HISB ↔ Norm | 0.198 | 0.056 | 3.545 | *** |
Source: Authors’ own creation
Model latent variables
| Latent variable | Items and scoring |
| oHISB | Consisted of three items measured using a five-point Likert scale, which ranged from “never” to “always”. The items were, “I check the reliability of online health information sources”, “My anxiety/worry is eased by online symptom searches” and “I feel reassured knowing that health symptoms I experience are typical to pregnancy” |
| Data saturation | Consisted of three items measured on an “always” to “never” Likert scale. The items were, “I feel as though I can never have too much information on a health issue I am experiencing or have experienced” (reverse score), “I continue to search for online health information after my health query has been answered” and “I repeat an online health search using a different source (E.g., Blogs, social media, government health sites)” |
| Trust | Consisted of three items measured on a five-point Likert scale, ranging from “extremely unlikely” to “extremely likely”. The items were, “I trust all online health information” (reverse score), “I trust other people’s personal accounts of health issues (E.g., blogs, vlogs, personal social media etc.)” (reverse score) and “I would use the internet to check that the doctor is giving me appropriate advice” |
| Normalisation | Consisted of three items measured using a five-point Likert scale. The three items were, “The internet is a good way of finding other people who are experiencing similar health problems”, “It can be helpful to see other people’s health-related experiences on the internet” and “Looking at health websites reassure me that I am not alone with my health concerns” were measured using a scale of “strongly disagree” to “strongly agree” |
| Offline support | Consisted of three items measured on a five-point Likert scale ranging from “strongly disagree” to “strongly agree”. The items were as follows; “I would use the internet if I need to make a decision about my health (for example, whether I should see a doctor, take medication or seek other types of treatment”, “The internet can be useful to help people decide if their symptoms are important enough to go to see a doctor” and “The internet is useful if you don’t want to tell people around you (for example, your family or people at work) how you feel” |
Source: Authors’ own creation
| Fit indexes | ||||||
|---|---|---|---|---|---|---|
| Df | χ² | GFI | TLI | CFI | RMSEA | |
| 145 | 302.292 | 0.856 | 0.791 | 0.823 | 0.076 | |
| Regression weights | ||||||
| Estimate | S.E. | C.R. | P | Label | ||
| Online_Platform | <--- | Norm | 1.000 | |||
| Offline_Platform | <--- | Trust. | 1.000 | |||
| Online_Platform | <--- | Data_Saturation | 1.000 | |||
| Online_Platform | <--- | Online_HISB | 1.000 | |||
| Offline_Platform | <--- | Offline_Support | 1.000 | |||
| Q10 | <--- | Online_HISB | 1.000 | |||
| Q13 | <--- | Online_HISB | 1.512 | 0.608 | 2.488 | 0.013 |
| Q15 | <--- | Online_HISB | 0.421 | 0.408 | 1.031 | 0.302 |
| Q17 | <--- | Online_HISB | 2.056 | 0.757 | 2.715 | 0.007 |
| Q8 | <--- | Data_Saturation | 1.000 | |||
| Q9 | <--- | Data_Saturation | −0.914 | 0.125 | −7.301 | *** |
| Q11 | <--- | Data_Saturation | 0.454 | 0.087 | 5.228 | *** |
| Q12 | <--- | Data_Saturation | 0.428 | 0.098 | 4.369 | *** |
| Q14 | <--- | Trust. | 1.000 | |||
| Q16 | <--- | Trust. | 1.308 | 0.286 | 4.581 | *** |
| Q23 | <--- | Trust. | −0.780 | 0.246 | −3.167 | 0.002 |
| Q18 | <--- | Norm | 1.000 | |||
| Q19 | <--- | Norm | 0.968 | 0.079 | 12.305 | *** |
| Q20 | <--- | Norm | 0.707 | 0.067 | 10.541 | *** |
| Q21 | <--- | Offline_Support | 1.000 | |||
| Q22 | <--- | Offline_Support | 0.845 | 0.099 | 8.498 | *** |
| Q25 | <--- | Offline_Support | 0.353 | 0.086 | 4.108 | *** |
| Q26 | <--- | Offline_Support | 0.289 | 0.124 | 2.336 | 0.019 |
| Skilful_Surfing | <--- | Online_Platform | 1.000 | |||
| Skilful_Surfing | <--- | Offline_Platform | 1.000 | |||
| Q24 | <--- | Trust. | −1.026 | 0.234 | −4.385 | *** |
| Covariances | ||||||
| Trust. | <--> | Offline_Support | −0.285 | 0.069 | −4.125 | *** |
| Online_HISB | <--> | Offline_Support | 0.074 | 0.037 | 2.006 | 0.045 |
| Norm | <--> | Offline_Support | 0.314 | 0.064 | 4.943 | *** |
| Trust. | <--> | Norm | −0.182 | 0.048 | −3.785 | *** |
| Online_HISB | <--> | Trust. | −0.049 | 0.025 | −1.951 | 0.051 |
| Online_HISB | <--> | Data_Saturation | −0.010 | 0.027 | −0.365 | 0.715 |
| Online_HISB | <--> | Norm | 0.144 | 0.054 | 2.681 | 0.007 |
| Trust. | <--> | Offline_Support | −0.232 | 0.068 | −3.403 | *** |
| Online_HISB | <--> | Offline_Support | 0.101 | 0.044 | 2.308 | 0.021 |
| Norm | <--> | Offline_Support | 0.315 | 0.064 | 4.913 | *** |
| Trust. | <--> | Norm | −0.191 | 0.054 | −3.555 | *** |
| Online_HISB | <--> | Trust. | −0.080 | 0.034 | −2.317 | 0.020 |
| Online_HISB | <--> | Data_Saturation | −0.026 | 0.017 | −1.535 | 0.125 |
| Online_HISB | <--> | Norm | 0.198 | 0.056 | 3.545 | *** |
Source: Authors’ own creation
Appendix 1
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Ghiasi, A. (2021), “Health information needs, sources of information, and barriers to accessing health information among pregnant women: a systematic review of research”, The Journal of Maternal-Fetal and Neonatal Medicine, Vol. 34 No. 8, pp. 1320-1330.
Acknowledgements
Author contributions: All authors contributed equally to the research.
Author conflicts of interest: All authors state no conflict of interest.