The rapid progression of science and technology has revolutionized the healthcare system worldwide. E-health or digital health is now becoming an essential part of contemporary healthcare organizations. E-health or digital health can be described as the cost-effective and secure utilization of information and communication technology in medicine and medical science to mitigate health risks and promote wellness (WHO, 2024). These digital tools have become an inseparable part of the maintenance of communicable and non-communicable diseases. For example, people are using smartwatches to monitor their blood pressure, heart rate, calorie consumption, and physical exercise (Masoumian Hosseini et al., 2023).

According to WHO, “E-Health encompasses multiple interventions, including telehealth, telemedicine, mobile health (mHealth), electronic medical or health records (eMR/eHR), big data, wearables, and even artificial intelligence” (WHO, 2024). It comprises internet use/browsing to get any information, such as sending emails, using wearable devices (e.g, smartwatches), operating smartphones, computers, and even searching for the digital health record from healthcare providers/hospitals. The utilization of digital health or E-health tools vary across communities due to the influence of the social determinants of health (SDoH). SDOH are the nonmedical factors- the conditions in which people are born, grow, work, live, worship, and age. These conditions include a wide set of forces and systems that shape daily life, such as economic policies and systems, development agendas, social norms, social policies, and political systems (Social Determinants of Health (SDOH), 2024). It includes geographic location, socioeconomic condition, literacy, race/ethnicity, food habits, language, and population demographic factors (Larnyo et al., 2025; Vaidhyam & Huang, 2023).

Researchers found that an individual’s income and geographic location determine access to the healthcare infrastructures, while digital literacy empowers the user’s online health information access effectively (Guo et al., 2022; McMaughan et al., 2020). Digital literacy and individual income are both equally important in the utilization of health technology and devices, such as smartwatches, computers, and access to the internet (American Cancer Society, 2023; WHO, 2024).Moreover, the geographic location of any population plays an important role in people lives in terms of their education, food habits, access to healthcare, housing, and other basic needs. On one hand, a digital health system can provide accessibility to virtual healthcare facilities in remote and underserved areas. However, utilizing e-health services may be challenging due to unstable internet connectivity in rural areas, besides lack of technical infrastructures, indicating the existence of disparities among urban and rural areas (Salehi et al., 2019).      

Research about digital health use among a specific population of cancer patients exists, especially among breast cancer patients (Almoajel et al., 2022; Igiraneza et al., 2021). It is true that early detection of a breast nodule or a lump can help individuals and healthcare providers to plan effective, life-saving breast cancer management (Meneses et al., 2023; Rizalar et al., 2014). Patients can get similar benefits for early detection of cervical cancer through routine Pap smear tests (Zhang et al., 2023). All this information is available to people at their fingertips only because of the tremendous progress of the internet and digital health. Hence, it is unknown how many individuals in the population get the appropriate and correct information, and how many of them are aware of how to utilize the source (Verbunt et al., 2022; Zhang et al., 2023).

The knowledge of proper use of digital health or E-health among healthcare providers needs to be studied for service enhancement (Verbunt et al., 2022). Previously, no research has been conducted to examine e-health utilization among the population according to their social determinants of health. Therefore, we aim to identify the most influential SDoH components related to digital health use and applications to plan, design, and implement effective, targeted interventions and inclusive approaches to improve the quality of life for all.

Methods

This cross-sectional study utilizes data from the Health Information National Trends Survey (HINTS) 6 dataset, which was completed in 2022 and updated in 2024.

The National Cancer Institute (NCI) of the USA launched HINTS in 2002 and 2003 to collect population-level estimates of individuals >18 years and to monitor changes in the rapidly evolving field of health communication for specific outcomes. The HINTS survey is repeated every two years with updated questionnaires; a section on telehealth was added after the COVID-19 Pandemic.

Initially, 6,252 participants aged 18 years and above responded; then 6,185, and 67 partially responded. The information was collected using a self-administered questionnaire. HINTS survey was conducted nationwide; therefore, it includes variables related to gender, race, level of education, income, geographic location and other social determinants of health components. For the analysis, we included all participants except those who did not fill out their questionnaire completely; these were considered as missing data.

Ethical Approval: This survey was conducted by NCI, and written consent was obtained from all participants.

The analysis was designed to systematically examine the influence of social determinants of health (SDoH) on three core aspects of e-health utilization: (1) seeking cancer information, (2) internet use, and (3) the use of wellness applications and wearable devices.

Data analysis was conducted using the statistical program R; The descriptive study was done for independent variables for demographic characteristics- age (years); birth gender (female/male); marital status (married/divorced/widowed/separated/single);occupation(employed/unemployed/student/homemaker/retired/disabled/others); and level of education (up to high school/college/postgraduate/others); income in range of ($0 to $19,999/$20,000 to $74,999/$75,000 or more).

The bivariate associations between SDoH variables and each e-health outcome for use of the internet (for example, emailing, using social media, etc), seeking cancer information on the internet, and their behaviour of using wearable devices such as smart watches. Logistic regression models were used to calculate crude odds ratios (ORs) and 95% confidence intervals (CIs) for categorical predictors.

To identify independent determinants of e-health utilization, multivariable logistic regression models were constructed for each outcome variable. Separate models were developed for three variables: 1) Seeking cancer information, 2) Internet use, and 3) Wellness application/wearable device use. Adjusted odds ratios (aOR) with 95% CIs and significance at p < 0.10 were entered into the respective models.

Results

The study population included 6,369 people, which was relatively balanced across age groups, with adults aged 50–64 years at 27.26%; followed by those aged 18–34 years (25.91%); 35–49 years (25.26%); and 21.57% of older adults aged 65 years and above. The males comprised 49.24% and females 50.76%. Most respondents identified as White only (73.42%), while Black only participants accounted for 12.75%, and other races—including American Indian, Asian, Pacific Islander, and multiracial groups—accounted for 13.83%. Over half of respondents were married (55.91%), while 31.22% reported being unmarried (never married/single). A smaller proportion, 12.87%, were formerly married, encompassing widowed, separated, or divorced individuals.

Educational levels varied widely. The largest subgroup reported vocational or technical training (32.96%), followed by those with up to high school education (31.23%). College graduates comprised 20.73%, and 15.08% held postgraduate degrees. Employment status revealed that a majority of respondents were employed (55.18%). Retirees constituted 19.79%, while unemployed individuals were only 3.02%. An additional 22.01% were classified as other occupations, including homemakers, students, or those with disabilities.

The sample was largely socioeconomically advantaged, with 43.13% reporting annual household incomes of $75,000 or more. A further 39.89% fell within the middle-income range ($20,000–$74,999), while 16.98% reported incomes below $20,000. The largest geographic representation was from the South Atlantic region (20.97%), followed by the Pacific (15.56%) and East North Central (14.11%) regions. Smaller proportions resided in New England (5.01%), East South Central (5.35%), and West North Central (6.55%). This indicates broad geographic coverage, though with clustering in the South Atlantic and Pacific regions.

Approximately 10.12% of respondents reported a history of cancer diagnosis (any type). A very high proportion of respondents reported internet use (85.81%), demonstrating near-universal connectivity in the sample. However, less than half (43.31%) reported actively searching for cancer information online, suggesting a gap between internet access and proactive engagement with health-specific information. In terms of technology-based health management, 56.58% of respondents reported using wearable wellness applications or devices, highlighting a substantial uptake of digital health tools. Only 10.12% of the whole sample population had cancer of any type.

Table 1: Multivariable Logistic Regression of SDoH Predictors of E-Health Utilization.

                                                    Seek Cancer Info           Use Wellness Apps              Internet Use         

Exposure variables

                                                    OR (95% CI)                    OR (95% CI)                        OR (95% CI)

Age group

35-49 Years                             1.3 (0.91 – 1.85)              1.01 (0.72 – 1.43)               0.94 (0.49 – 1.78)

50-64 Years                             1.48 (1.08 – 2.04) *            0.68 (0.49 – 0.96) *                        0.43 (0.25 – 0.76) **

65+ years                                 1.17 (0.77 – 1.76)            0.38 (0.25 – 0.59) **             0.14 (0.08 – 0.24) **

Birth Gende

Male                                        0.48 (0.39 – 0.58) **           0.65 (0.50 – 0.85) **                       0.88 (0.66 – 1.16)

RACE

White only                             1.56 (1.13 – 2.15) *              1.36 (0.93 – 1.99)          1.77 (1.31 – 2.40) **

Other races                            1.04 (0.67 – 1.63)               1.33 (0.81 – 2.19)            0.87 (0.46 – 1.65)

Marital Status

Married                                                 1.23 (0.95 – 1.60)               1.05 (0.80 – 1.39)             1.23 (0.88 – 1.73)

Unmarried                              0.86 (0.60 – 1.23)              0.66 (0.43 – 1.02)            1.09 (0.69 – 1.72)

Level of education

Postgraduate                         1.38 (1.08 – 1.77) *             1.39 (0.97 – 1.97)         1.72 (0.88 – 3.36)

Up to Highschool                                0.34 (0.26 – 0.47) **           0.37 (0.26 – 0.54) *          0.27 (0.17 – 0.42) **

Vocational or technical        0.78 (0.58 – 1.04)                0.78 (0.57 – 1.05)                         0.93 (0.53 – 1.63)

Occupation

Other occupations                1.19 (0.87 – 1.63)                0.94 (0.65 – 1.36)           0.84 (0.54 – 1.31)

Retired                                    1.3 (0.89 – 1.92)                  0.82 (0.58 – 1.16)           1.25 (0.77 – 2.05)

Unemployed                           0.78 (0.44 – 1.40)               0.52 (0.26 – 1.05)           1.1 (0.42 – 2.92)

Income

$20,000 to $74,999               1.24 (0.80 – 1.91)                1.63 (1.00 – 2.65)           1.75 (1.21 – 2.53) **

$75,000 or more                                  1.72 (1.03 – 2.87) *               2.31 (1.40 – 3.80) *           3.74 (2.33 – 6.00) **

Geographic location

Middle Atlantic                        0.54 (0.29 – 0.98) *          0.79 (0.47 – 1.35)            0.91 (0.50 – 1.65)

East North central                   0.57 (0.32 – 1.02)             0.75 (0.44 – 1.28)            0.84 (0.41 – 1.70)

West North central                 0.5 (0.26 – 0.97) *            1.07 (0.50 – 2.29)            0.82 (0.39 – 1.75)

South Atlantic                          0.61 (0.33 – 1.13)             0.96 (0.56 – 1.66)            1.5 (0.80 – 2.83)

East South central                   0.59 (0.27 – 1.28)             0.47 (0.19 – 1.15)            0.97(0.50 – 1.91)

West South central                 0.73 (0.39 – 1.38)             0.86 (0.50 – 1.49)            1.25 (0.68 – 2.27)

Mountain                                 0.74 (0.37 – 1.46)             1.3 (0.67 – 2.53)              1.1 (0.52 – 2.33)

Pacific                                        0.53 (0.30 – 0.95) *          0.8 (0.43 – 1.49)              1.25 (0.62 –2.51)