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Short Paper (3) eHealth Literacy / Digital Literacy (538) Colon and Rectal Cancer (50) Equity and Digital Divide (379) Use and User Demographics of mHealth (407)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67911, first published .
Correlates and Detection of Digital Health Literacy in Patients With Colorectal Carcinoma or Non-Hodgkin Lymphoma: Cross-Sectional Study

Correlates and Detection of Digital Health Literacy in Patients With Colorectal Carcinoma or Non-Hodgkin Lymphoma: Cross-Sectional Study

Correlates and Detection of Digital Health Literacy in Patients With Colorectal Carcinoma or Non-Hodgkin Lymphoma: Cross-Sectional Study

Authors of this article:

Thomas S Gunning1, 2 Author Orcid Image ;   Amanda Khoudary3 Author Orcid Image ;   Osairys Billini3 Author Orcid Image ;   Andrew Ip1, 4 Author Orcid Image ;   Lia Sorgen5 Author Orcid Image ;   John Marshall5 Author Orcid Image ;   Benjamin A Weinberg5 Author Orcid Image ;   Arnold L Potosky5 Author Orcid Image ;   Marc D Schwartz5 Author Orcid Image ;   Claire C Conley5 Author Orcid Image ;   Heather M Derry-Vick1, 3 Author Orcid Image
Article Authors Cited by Tweetations (4) Metrics

    1Hackensack Meridian School of Medicine, Nutley, NJ, United States

    2Department of Internal Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States

    3Cancer Prevention Precision Control Institute, Center for Discovery and Innovation, Hackensack Meridian Health, 123 Metro Blvd, Nutley, NJ, United States

    4John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, United States

    5Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States

    Corresponding Author:

    Heather M Derry-Vick, PhD


    Background: Cutting-edge oncology care often depends on patients’ ability to use rapidly evolving health technology. Digital health literacy (DHL; the capacity to understand health-related information with electronic media) is an emerging, yet underexplored social determinant of health in patients with cancer.

    Objective: We aimed to characterize sociodemographic and clinical factors associated with DHL in patients with cancer and explore whether a single-item screener could be derived from a widely-used DHL questionnaire to detect low DHL.

    Methods: Patients (N=105) who received systemic treatment in the past year for colorectal carcinoma (CRC) or non-Hodgkin lymphoma (NHL) were recruited through collaborating clinics. Participants self-reported DHL using the eHealth Literacy Scale (eHEALS). They also reported general health literacy and sociodemographic and clinical characteristics. Correlations and group comparisons (independent sample t tests and χ2 tests, as appropriate) were used to evaluate links between DHL and sociodemographic and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was used to determine whether a single eHEALS item could effectively screen for low DHL (eHEALS score ≤20).

    Results: Patients with a lower education level (Spearman ρ=0.29; P=.004) and lower general health literacy (r=0.25; P=.009) had lower DHL. Patients with NHL reported lower DHL than those with CRC (t103=2.72; P=.008). Additionally, the subset of patients who reported participation in a clinical trial (n=10) exhibited lower DHL than nonparticipants (t100=3.08; P=.003). Other sociodemographic and clinical characteristics were not significantly associated with DHL (all P>.21). The ROC curve analysis showed that eHEALS item 4 (“I know where to find helpful health resources on the Internet”) was a strong predictor of high versus low DHL (area under the curve=0.975, 95% CI 0.949‐1.00; P<.001).

    Conclusions: In this convenience sample, DHL varied based on cancer type, education level, general health literacy, and clinical trial participation. Furthermore, we found that a single item from the eHEALS has strong potential for identifying those with low DHL. These findings may inform which patients have higher need for or may benefit from DHL interventions and suggest avenues for detecting low DHL in oncology clinics.

    JMIR Cancer 2025;11:e67911

    doi:10.2196/67911

    Keywords

    digital health literacy; eHealth; screener; screening; colorectal carcinoma; non-Hodgkin lymphoma 


    Physicians, caregivers, and cancer survivors increasingly use technology as part of cancer care [1,2]. Engagement with health technologies (eg, telemedicine and patient portals) confers substantial benefits [3-5]; they facilitate access to care, increase patient engagement, and help patients manage, coordinate, and learn about their health [6-8]. However, accelerating health technology availability has increased the “digital divide” between patients who adopt health technology as part of their care and those who do not [9-11].

    Digital health literacy (DHL; the capacity to find and understand health-related information with electronic media) is an emerging social determinant of health (SDOH) [12,13]. Lower DHL is associated with health risks and may impede uptake of cutting-edge patient-centered technologies aimed to optimize cancer care [14,15]. In a recent retrospective study of patients with cancer, those with higher DHL showed better overall survival than those with lower DHL [16].

    Promoting equitable cancer outcomes across DHL levels calls for new methods to quickly identify low DHL among patients with cancer [17]. Accordingly, we investigated how sociodemographic and clinical factors were related to patients’ reported DHL using data from a multicenter cross-sectional study of patients with non-Hodgkin lymphoma (NHL) or colorectal carcinoma (CRC). We also examined whether a single-item screener could be derived from a widely used DHL questionnaire to maximize screening opportunities in the oncology clinic and inform future studies.


    Study Procedures

    Patients (N=105) who received systemic therapy (eg, chemotherapy or immunotherapy) within the last year for NHL or CRC were recruited between November 2022 and April 2023 from 3 clinics at MedStar Georgetown University Hospital, MedStar Washington Hospital Center, and John Theurer Cancer Center as part of a larger cross-sectional study about cancer survivorship experiences. The study’s methods were previously published, and the current analysis represents a secondary analysis of these data [18]. NHL and CRC were selected to represent both solid and hematological cancers and due to their high prevalence at collaborating clinics, frequent use of systemic therapies, and prevalence across age and gender groups (versus, eg, breast or prostate cancer) [19]. Participants reported DHL on the validated eHealth Literacy Scale (eHEALS) by rating their ability or confidence to complete digital health tasks on 8 Likert-scale items (score range: 8-40) [20,21]. Scores ≤20 indicate low DHL [22-24]. Cronbach α was 0.96 in this sample. Participants reported their age, gender, race, ethnicity, education, general health literacy (with the 3-item Health Literacy Screening questionnaire [25]), and disease- and treatment history–related clinical factors. Surveys were mailed or emailed to the patient and were completed on paper, online, or over the phone.

    Statistical Analysis

    Statistical analysis used RStudio (version 2023.12.0+369) and GraphPad-Prism (version 10.3.1). We computed descriptive statistics, then used independent-sample t tests, χ2 tests, and correlations (as appropriate) to test associations between sociodemographic or clinical factors and DHL, with a 2-tailed α level of .05. We used simultaneous multiple linear regression to evaluate whether associations between clinical factors and DHL remained when adjusting for covariates (age, education level, and general health literacy; these were selected according to prior work or significant relationships with DHL in this dataset at P<.05) [13,15,26,27].

    To identify how single items performed to identify low DHL, we generated receiver operating characteristic (ROC) curves for each eHEALS item using a previously described approach [28,29]. We examined the area under the curve (AUC) for each ROC curve to compare the overall performance of each item for predicting low DHL.

    Ethical Considerations

    The Georgetown-MedStar Joint Oncology Institutional Review Board (STUDY00005586) approved the study. All participants provided informed consent. Study procedures included standard practices to prioritize participants’ privacy and confidentiality, including labeling surveys with participant IDs rather than identifiers, secure data storage, deidentification prior to analysis, and aggregate reporting. A US $20 gift card was offered to all participants.


    Participant Characteristics

    Among the 105 participants (Table 1), 50 (48%) had NHL and 55 (52%) had CRC. Participants were primarily middle-aged to older (mean age 60, SD 13.2, range 22‐84 years), male (n=57, 54%), identified as White (n=65, 62%), and had a college-level education or higher (n=57, 54%). The mean eHEALS score was 26.9 (SD 8.24, range 8‐40), and 24% (n=25) had low DHL (score ≤20).

    Table 1. Patient characteristics and differences between patients with colorectal carcinoma (CRC) and non-Hodgkin lymphoma (NHL) assessed in this study.
    Total (n=105)CRC (n=55)NHL (n=50)P value
    eHEALSa score, mean (SD)26.9 (8.24)28.9 (7.91)24.6 (8.10).008
    Health literacy score, mean (SD)12.6 (2.42)12.7 (2.32)12.5 (2.54).57
    Age (years), mean (range)60.3 (22-84)57.1 (31-84)62.9 (22-82).05
    Female, n (%)48 (46)29 (53)19 (38).13
    Race/ethnicityb, (%)<.001
    Asian, (%)7 (7)5 (9)2 (4)
    Black or African American26 (25)23 (42)3 (6)
    Hispanic or Latino2 (2)1 (2)1 (2)
    Middle Eastern or North African4 (4)1 (2)3 (6)
    White65 (62)25 (45)40 (80)
    Other or unknown4 (4)3 (5)1 (2)
    Education, n (%).03
    High school or less20 (19)10 (18)10 (20)
    Some college22 (21)7 (13)15 (30)
    College degree21 (20)10 (18)11 (22)
    Graduate degree36 (34)26 (47)10 (20)
    Other or unknown6 (6)2 (4)4 (8)
    Months from diagnosis, mean (SD)50.9 (127.5)36.3 (39.7)66.9 (179.7).22
    Treatment type, n (%)
    Infusion therapy99 (94)52 (95)47 (94).90
    Oral medication62 (59)34 (62)28 (56).54
    Radiation therapy23 (22)21 (38)2 (4)<.001
    Clinical trial participation, n (%)10 (9.5)4 (7.3)6 (12).41

    aeHEALS: eHealth Literacy Scale.

    bParticipants could select more than one option for race/ethnicity.

    Sociodemographic and Clinical Factors Associated With DHL

    Those with lower education levels and lower general health literacy had lower DHL (Spearman ρ=0.29; P=.004 and r=0.25; P=.009, respectively). DHL did not vary by age, gender, race/ethnicity, or household income (all P>.21).

    Patients with NHL reported lower DHL than patients with CRC (t103=2.72; P=.008; Figure 1A). This pattern persisted when controlling for age, education, and general health literacy (unstandardized b coefficient=−3.07, SE=1.65; P=.07). The patient subset who participated in a clinical trial (n=10) as part of their cancer treatment had lower DHL than patients who did not participate in a trial (t100=3.08; P=.003; Figure 1B). In the adjusted model, patients reporting trial participation still showed lower DHL when controlling for age, education, general health literacy, or disease type (b=−10.45; SE=3.20; P=.002; Table 2). DHL did not vary by time since diagnosis or treatment type (all P>.23).

    ‎
    Figure 1. Clinical factors associated with digital health literacy (DHL) among participants. (A) Patients with colorectal carcinoma (CRC) reported higher DHL than patients with non-Hodgkin lymphoma (NHL) (P=.008). (B) Patients who reported participating in a clinical trial reported lower DHL than patients who did not (P=.003). eHEALS: eHealth Literacy Scale. **indicates P-value less than 0.05.
    Table 2. Multivariable linear regression model predicting eHealth Literacy Scale score as the outcome.
    VariablebSEP value
    Clinical trial participation–10.43.20.002
    Disease type (ref CRCa)–3.201.60.049
    Age–0.020.06.74
    Educational achievement level1.670.72.02
    General health literacy0.690.34.046

    aCRC: colorectal carcinoma.

    Single-Item Screener for Low DHL

    In an ROC analysis predicting low DHL (eHEALs ≤20), individual items’ AUC values ranged from 0.895 to 0.975 (Table 3, Figure S1 in Multimedia Appendix 1). eHEALS item 4, “I know where to find helpful health resources on the Internet,” had the highest AUC (0.975, 95% CI 0.949‐1.00; P<.001; Figure 2A). Responses to item 4 were strongly correlated to overall eHEALS scores (r=0.926; P<.001; Figure 2B). We determined the optimal threshold score for this item to predict high versus low DHL based on accuracy (Table S1 in Multimedia Appendix 1). Using the Likert-scale options (ranging 1-5), a score less than 3 predicted low DHL with an accuracy of 96.2%, a sensitivity of 94.94% (95% CI 87.69‐98.01) and a specificity of 96.15% (95% CI 81.11‐99.80).

    Table 3. eHealth Literacy Scale (eHEALS) items’ area under the curve (AUC) values, calculated with receiver operating characteristic curve analysis. The items come from the eHEALS, developed by Norman and Skinner [20].
    ItemDefinitionAUC (95% CI)P value
    1I know how to find helpful health resources on the Internet0.901 (0.828‐0.974)<.001
    2I know how to use the Internet to answer my health questions0.939 (0.882‐0.996)<.001
    3I know what health resources are available on the Internet0.953 (0.904‐1.00)<.001
    4I know where to find helpful health resources on the Internet0.975 (0.949‐1.00)<.001
    5I know how to use the health information I find on the Internet to help me0.935 (0.873‐0.996)<.001
    6I have the skills I need to evaluate the health resources I find on the Internet0.940 (0.890‐0.990)<.001
    7I can tell high quality from low quality health resources on the Internet0.949 (0.910‐0.988)<.001
    8I feel confident in using information from the Internet to make health decisions0.895 (0.831‐0.959)<.001
    ‎
    Figure 2. (A) Receiver operator characteristic curve for eHealth Literacy Scale (eHEALS) item 4 detects high and low digital health literacy with high sensitivity and specificity (P<.001). (B) Correlation between eHEALS item 4 and participants’ overall eHEALS scores (P<.001).

    Principal Findings

    DHL allows patients to benefit from technological advances in oncology care. However, little is known about which patients with cancer are more likely to experience low DHL, and clinically feasible ways to identify patients with low DHL are limited. Here, we examined sociodemographic and clinical factors associated with DHL and explored whether single items could identify patients with low DHL. Patients with a lower education level and general health literacy had lower DHL. Patients with NHL also had lower DHL than those with CRC. Interestingly, those who reported participating in a clinical trial had lower DHL than those who did not. ROC analysis showed that agreement with a single item from the widely used eHEALS (“I know where to find helpful health resources on the Internet”) was strongly predictive of low DHL; this may be a brief yet sensitive way to screen for low DHL.

    Links between DHL and sociodemographic factors have previously been reported [13,15,26,27], but clinical correlates have been less frequently explored. We observed notable differences in DHL between patients with CRC and NHL that remained after adjusting for covariates. Future studies should identify drivers of DHL differences by cancer type in larger samples. We observed a novel, unexpected association between lower DHL and clinical trial participation, contrasting with prior findings that clinical trial participants used the internet first, before physicians, for clinical trial information [30]. On the other hand, it is possible that patients with worse DHL have poorer outcomes, thus necessitating later lines of therapy and enrollment in clinical trials, which could help to explain our findings. The complex relationship between clinical trial participation and DHL requires further investigation in prospective studies with electronic medical record–documented trial participation.

    Rapidly and effectively identifying those with low DHL is critical to advancing this research area. Short-form screeners are well-suited for time-limited oncology clinics [29,31]. Our findings indicate that disagreeing or strongly disagreeing with the statement “I know where to find helpful health resources on the Internet” is a candidate screening criterion for low DHL. If validated in larger samples and other cancers, the item could be included in previsit paperwork, and identified patients with low DHL could receive additional support (eg, for telehealth and patient portals) or alternative modalities (eg, print resources). Future studies could also use this item to quickly screen for digital health needs in study candidates.

    Limitations

    This study’s small convenience sample may limit its statistical power to identify associations and may restrict generalizability. Although we included people with solid and hematological cancers, correlates and best screening items for DHL may differ across other cancer types. Nonetheless, the observed links to sociodemographic factors are consistent with prior literature, and the full range of possible eHEALS scores (8-40) was represented in our sample. Future studies should include larger samples, more clinical trial participants, and more diverse sociodemographic and cancer groups. Additionally, future studies should include family members and other care partners, who have an important role in helping patients with lower DHL navigate their care.

    Conclusions

    DHL is an emerging and actionable SDOH impacting health outcomes for people with cancer. These results provide insight into how DHL varies across cancer patients and suggest a highly predictive single-item screener that may be used to identify patients with low DHL. Using this item, if it is further validated, could increase opportunities for clinical support and developing DHL interventions better suited to patient needs.

    Acknowledgments

    We would like to acknowledge the patients, clinicians, and cancer team members that made this project possible. We would also like to acknowledge the Hackensack Meridian School of Medicine Human Dimension program for facilitating the early stages of this work. Work on this study was supported by the National Cancer Institute (K08CA270402 [principal investigator CCC] and P30CA051008 [principal investigator Louis M Weiner, MD]) and the Jess and Mildred Fisher Center for Hereditary Cancer and Clinical Genomics Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. At no point in the conception, planning, analysis, or writing of this manuscript was generative artificial intelligence used.

    Data Availability

    Deidentified data are publicly available on Open Science Framework [32]. Sociodemographic data are omitted for privacy reasons and can be requested from the corresponding author.

    Authors' Contributions

    Conceptualization and methodology: TSG, ALP, MDS, CCC, HMD-V

    Funding acquisition: ALP, MDS, HMD-V

    Supervision: ALP, MDS, CCC, HMD-V

    Project administration: AK, LS, HMD-V, CCC

    Formal analysis: TSG, AK

    Investigation: LS, AK, OB

    Resources: AI, JM, BAW

    Data curation: TSG, AK

    Visualization: TSG, AK

    Writing—original draft: TSG, HMD-V

    Writing—review and editing: all authors

    At no point in the conception, planning, analysis, or writing of this manuscript was generative AI used.

    Conflicts of Interest

    AI declares receiving honoraria from, working as a consultant for, and/or serving on speakers’ bureaus of MJH Life Sciences, Graticule, AbbVie, Pfizer/Seagen, Kite/Gilead, Curio Life Sciences, and AstraZeneca, as well as serving on the advisory boards of Pfizer/Seagen and Kite/Gilead and owning stock in COTA. CCC reports receiving research funding from Pfizer.

    Multimedia Appendix 1

    Receiver operating characteristic curves for each eHealth Literacy Scale item; sensitivity and specificity for item 4.

    DOCX File, 176 KB
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    ‎
    AUC: area under the curve
    CRC: colorectal carcinoma
    DHL: digital health literacy
    eHEALS: eHealth Literacy Scale
    NHL: non-Hodgkin lymphoma
    ROC: receiver operator characteristic
    SDOH: social determinant of health

    Edited by Naomi Cahill; submitted 15.Apr.2025; peer-reviewed by Oghosa Evbuomwan; final revised version received 23.Sep.2025; accepted 25.Sep.2025; published 14.Nov.2025.

    Copyright

    © Thomas S Gunning, Amanda Khoudary, Osairys Billini, Andrew Ip, Lia Sorgen, John Marshall, Benjamin A Weinberg, Arnold L Potosky, Marc D Schwartz, Claire C Conley, Heather M Derry-Vick. Originally published in JMIR Cancer (https://cancer.jmir.org), 14.Nov.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.