Abstract
The number of social media users has increased substantially in the past decade, creating an opportunity for health-care professionals and patients to leverage social media for health communication. This study examines the recent use and predictors of social media for health communication in a nationally representative sample of US adults over time. We used 2013, 2014, and 2017 National Cancer Institute’s Health Information National Trends Survey to identify respondents’ use of social media for sharing health information or exchanging medical information with a health-care professional. We conducted bivariate analysis using the Pearson χ2 test to assess the association of respondents’ basic demographic characteristics as well as health status and the use of social media for health communication. We performed multivariable logistic regression models to examine factors associated with the use of social media for health communication. We identified 4242 respondents (weighted sample size: 343 465 241 [2-year pooled sample]) who used social media for sharing health information and 4834 respondents (weighted sample size: 354 419 489 [2-year pooled sample]) who used social media for exchanging medical information. Multivariable analyses indicated the proportion of respondents who used social media for sharing health information has decreased (odds ratio [OR], 0.65; 95% confidence interval [CI], 0.49-0.85, P = .002), while the use of social media for exchanging medical information with a health-care professional has increased (OR, 1.88; 95% CI, 1.09-3.26, P = .025). The younger population had significantly higher odds of using social media for health communication. The study found no racial/ethnic disparities in the use of social media for health communication. Use of social media for sharing health information has declined, while exchanging medical information with health-care professionals has increased. Future research is needed to determine how to engage the population in social media–based health interventions, particularly for older adults.
Keywords: social media, cancer, communication, health information, knowledge
Introduction
Use of social media has grown rapidly in the past 10 years. Currently, the monthly worldwide active users of 2 major social media platforms, Facebook and Twitter, have reached 2.20 billion and 336 million, respectively.1 The total number of users of all major social media platforms is predicted to be 3.02 billion by 2021.2 In the Unites States, the number of social media users is estimated to exceed 210 million in 2018,3 more than 4 times the number of social media users 10 years ago in 2009.4 With such a high penetration rate, social media offers many opportunities for health care. Studies have reported several uses of social media by health-care providers, such as dissemination of health information,5-7 monitoring of disease pandemics,8,9 health promotion,10,11 recruitment of clinical study participants,12-14 and social media–based health interventions.15-17
Although there have been a number of studies examining health-care providers’ use of social media in health care, less is known about the general population’s health-related social media use. A recent study surveying social media users found 4 primary reasons for health-related social media use: (1) to gain knowledge about their diagnosed disease; (2) to obtain advice from other patients with the same disease; (3) to receive social support; and (4) to communicate with a physician.18 The study also reported many barriers to health-related social media use in the general population, such as privacy concerns, unreliability of information, low perceived need, and inefficiency.18 It is unknown whether increased needs of social media in health communication have improved the use of social media, or these reported barriers have prevented the more widespread use of social media. To date, many of the studies examining population use of social media have been cross-sectional; therefore, it is unclear how the use of social media for health communication has changed over time. Given that the number of social media users is changing over time, it is important to understand whether the use of social media for health communication is also changing over time.
To address this gap, our study sought to determine the temporal trend and predictors of social media use for health communication, including sharing health information (general public forums) and exchanging medical information with health-care professionals (patient–provider forums) in a nationally representative sample of US adults. We also aimed to examine the association between survey respondent’s characteristics and temporal trends in use of social media. We hypothesized that the proportion of respondents who shared health information directly on a social media platform has decreased over time. Further, we hypothesized that the use of social media as a tool to exchange medical information with health-care professionals has not been impacted by privacy concerns because the public perceives health-care professionals as privacy aware. Because of the role of the health-care provider as an expert and trusted entity in providing advice on health-related issues and interpreting health-related information, another goal of our study was to examine the use of social media in health communication between patients and providers by comparison of these 2 types of social media use for health communication in general public forums versus patient–provider forums.
Methods
Data
This study used data from the National Cancer Institute’s (NCI’s) Health Information National Trends Survey (HINTS) administrated by the NCI’s Division of Cancer Control and Population Sciences. The HINTS collects nationally representative data about the changes in the rapidly evolving field of health communication through different communication channels. The HINTS data used a well-constructed sampling frame that collects data from all nonvacant residential addresses in the United States. The sampling frame of addresses was grouped into areas with high concentrations of minority population and areas with low concentrations of minority population, so HINTS can oversample the high-minority stratum to increase the precision of estimates for minority subpopulations. The full-sample weights in the survey can be used to provide valid inferences from the responding sample to the population. In this study, we used HINTS 4 Cycle 3 (2013), HINTS 4 Cycle 4 (2014), and HINTS 5 Cycle 1 (2017) data sets, since the questions about sharing health information through social media were collected in the years 2013 and 2017, and the questions about use of social media for exchanging medical information with health-care professionals were collected in the years 2013 and 2014. The 2013 cycle 3 survey data was collected from September 2013 through December 2013; the 2014 cycle 4 data were collected from August through November 2014; and the 2017 cycle 1 data were collected from January 25 through May 5, 2017.
Study Population
The study includes 2 study samples (Supplemental Table 1). The first sample completed the HINTS 4 cycle 3 and cycle 4 surveys to assess the use of social media for sharing health information. A total of 6470 respondents answered these 2 surveys. Respondents were excluded from the study if the question about social media use for sharing health information was not answered (n = 963) or if basic demographic information was missing, including age, sex, race, education, marital status, income, employment, and region (n = 1111). Respondents were also excluded if the health-related information was not reported, including health insurance, health status, smoking status, or cancer history (n = 154). The final first study sample included 4242 respondents (weighted sample: 343 465 241)—which pools 2013 and 2014 data. The second sample completed the HINTS 4 cycle 3 and HINTS 5 cycle 1 surveys to assess use of social media for exchanging medical information with a health-care professional. A total of 6862 respondents answered these 2 surveys. Respondents were excluded from the study if basic demographic information (as described earlier) was missing (n = 1793). Respondents were also excluded if health-related information was not reported (n = 235). The final second study sample included 4834 respondents (Weight sample: 354 419 489)—which pools 2013 and 2017 data.
Study Variables
Health communication using social media
The 2 dependent variables measuring social media use for health communication (eg, health information sharing and medical information exchange) were assessed using 2 HINTS survey questions. To assess health information sharing, respondents were asked, “In the last 12 months, have you shared health information on social networking sites, such as Facebook or Twitter”; to assess medical information exchange, respondents were asked, “In the past 12 months, have you used social media (e.g., Facebook, Google+, CaringBridge, etc.) to exchange medical information with a health care professional?” Respondents answered either “yes” or “no” to these 2 questions.
Sociodemographic variables
Respondents’ social demographics collected from multiple questions in HINTS include age, sex, race/ethnicity, education level, marital status, family income, employment status, and census region. The respondent’s age was grouped into 3 categories: young adults, 18 to 49 years; middle-aged adults, 50 to 64 years; and older adults, 65 years or older. Sex and employment status were measured as a binary variable. Although detailed race/ethnicity information was collected in the HINTS, such as Asian Americans, Native Americans, Native Hawaiian, and Pacific Islanders, due to small sample size in some of these subgroups, we classified respondents into four race/ethnicity groups: non-Hispanic white, non-Hispanic black, Hispanic, and other. The marital status in the survey includes married, divorced, widowed, separated, never married, or living with a partner. We grouped respondents’ marital status into 3 categories: single, married, and divorced/widowed/separated. The education level included less than high school, high school graduate, some college, and college or higher. The income variable has 5 levels: <US$20 000, US$20 000 to <US$35 000, US$35 000 to <US$50 000, US$50 000 to < US$75 000, and US$75 000 or more.
Health-related variables
Respondents’ access to health care was assessed by the question asking whether respondents have any health insurance coverage. The respondents’ health status was assessed by HINTS using a question with 5-point rating scale that ranged from “poor” to “excellent.” Respondent was classified into current, former, and never smokers using 2 smoking-related questions “Have you smoked at least 100 cigarettes in your entire life?” and “How often do you now smoke cigarettes?” We used 2 cancer-related questions to assess respondent’s personal and family history of cancer: “Have you ever been diagnosed as having cancer?” and “Have any of your family members ever had cancer?”
Cancer-related psychological variables
We assessed respondents’ knowledge and beliefs about cancer using 5 HINTS questions: (1) “How likely are you to get cancer in your lifetime?”(5-point rating scale ranged from “very unlikely” to “very likely”); (2) “How worried are you about getting cancer?” (5-point rating scale ranged from “not at all” to “extremely”); (3) “How much do you agree or disagree: It seems like everything causes cancer?”; (4) “How much do you agree or disagree: There’s not much you can do to lower your chances of getting cancer?”; and (5) “How much do you agree or disagree: In adults, cancer is more common than heart disease?” (4-point rating scale for all ranged from “strongly agree” to “strongly disagree”).
Statistical Analysis
We conducted bivariate analysis using the Pearson χ2 test to assess the association of respondents’ basic demographic characteristics as well as health status and social media use for sharing health information and exchanging medical information with a health-care professional. To examine the factors associated with sharing health information and exchanging medical information, we conducted multivariable logistic regression models. The χ2 test was used to test the association between the use of social media and cancer beliefs and knowledge.
All statistical tests were 2-sided, and all analyses were performed using SAS version 9.4 software (SAS Institute, Cary, North Carolina). Sampling weights calculated from the HINTS complex sample design were used in the statistical analysis using SAS. We used the SURVEYMEANS procedure to derive nationally representative estimates on the bivariate analysis and to generate standard errors and SURVEYLOGISTIC procedure for the multivariable logistic model. Statistical significance was defined as P < .05. This study was deemed exempt from review by the Institutional Review Board at the University of Florida.
Results
Use of Social Media for Sharing the Health Information
Demographics and health characteristics
We compared the use of social media for sharing health information by respondents’ demographic and health characteristics (Table 1). We observed that the use of social media for sharing health information had dropped significantly from the year 2013 (24.7%) to 2017 (15.7%; P < .001). Compared to the population aged 50 to 64 years old (11.6%) and the elderly population aged 65 years or older (5.5%), the younger population aged 49 or less has a significantly higher rate of social media use for sharing health information (26.4%; P < .001). The other factors associated with higher use of social media for sharing health information included female gender, a college education or higher, not being divorced/windowed/separated, being employed, or having a family history of any cancer.
Table 1.
Sociodemographic Characteristics of Survey Respondents.
| Health Information Share, N = 4242, Weighted N=343 465 241 (2-year pooled sample) | Medical Information Exchange, N = 4834, Weighted N=354 419 489 (2-year pooled sample) | |||||
|---|---|---|---|---|---|---|
| Yes | No | Yes | No | |||
| Unweighted population | 733 | 3509 | 136 | 4698 | ||
| Weighted population | 67 220 218 | 276 245 023 | P Value | 10 408 315 | 344 011 175 | P Value |
| Characteristics | No. (Col, %) | No. (Col, %) | No. (Col, %) | No. (Col, %) | ||
| Year | ||||||
| 2013 | 364 (24.7) | 1365 (75.3) | <.001 | 45 (2.0) | 2106 (98.0) | .025 |
| 2014 | - | - | 91 (3.8) | 2592 (96.2) | ||
| 2017 | 369 (15.7) | 2144 (84.3) | - | - | ||
| Age-group | ||||||
| 18-49 | 471 (26.4) | 1257 (73.6) | <.001 | 70 (3.5) | 1874 (96.5) | .022 |
| 50-64 | 192 (11.6) | 1294 (88.4) | 45 (2.4) | 1655 (97.6) | ||
| 65+ | 70 (5.5) | 958 (94.5) | 21 (1.5) | 1169 (98.5) | ||
| Sex | ||||||
| Male | 210 (13.7) | 1534 (86.3) | <.001 | 47 (2.7) | 1904 (97.3) | .518 |
| Female | 523 (25.4) | 1975 (74.6) | 89 (3.2) | 2794 (96.8) | ||
| Race/Ethnicity | ||||||
| Non-Hispanic white | 442 (20.0) | 2258 (80.0) | .735 | 67 (2.5) | 2829 (97.5) | .573 |
| Non-Hispanic black | 88 (17.1) | 480 (82.9) | 26 (3.3) | 717 (96.7) | ||
| Hispanic | 129 (18.5) | 492 (81.5) | 28 (4.1) | 787 (95.9) | ||
| Other | 74 (20.4) | 279 (79.6) | 15 (4.2) | 365 (95.8) | ||
| Education | ||||||
| Less than high school | 21 (11.0) | 185 (89.0) | .018 | 9 (2.2) | 354 (97.8) | .599 |
| High school graduate | 95 (16.4) | 596 (83.6) | 19 (2.1) | 880 (97.9) | ||
| Some college | 248 (23.5) | 1034 (76.5) | 46 (3.6) | 1448 (96.4) | ||
| College or higher | 369 (19.2) | 1694 (80.8) | 62 (3.0) | 2016 (97.0) | ||
| Marital status | ||||||
| Single | 141 (20.8) | 574 (79.2) | .022 | 31 (3.5) | 815 (96.5) | .680 |
| Divorced/widowed/separated | 153 (13.9) | 918 (86.1) | 38 (2.5) | 1338 (97.5) | ||
| Married | 439 (20.3) | 2017 (79.7) | 67 (2.8) | 2545 (97.2) | ||
| Family income | ||||||
| Less than US$20 000 | 125 (20.1) | 537 (79.9) | .960 | 36 (3.1) | 9992 (96.9) | .028 |
| US$20 000 to < US$35 000 | 92 (19.4) | 478 (80.6) | 17 (1.2) | 678 (98.8) | ||
| US$35 000 to < US$50 000 | 108 (21.1) | 464 (78.9) | 21 (3.0) | 692 (97.0) | ||
| US$50 000 to < US$75 000 | 120 (19.2) | 682 (80.8) | 16 (2.3) | 813 (97.7) | ||
| US$75 000 or More | 288 (19.1) | 1348 (80.9) | 46 (3.7) | 1523 (96.3) | ||
| Employment | ||||||
| Employed | 486 (21.3) | 2001 (78.7) | .043 | 78 (2.7) | 2617 (97.3) | .390 |
| Not employed | 247 (16.3) | 1508 (83.7) | 58 (3.4) | 2081 (96.6) | ||
| Census region | ||||||
| Northeast | 106 (15.5) | 597 (84.5) | .319 | 21 (2.5) | 744 (97.5) | .882 |
| Midwest | 140 (20.8) | 641 (79.2) | 26 (3.43 | 867 (96.7) | ||
| South | 304 (20.9) | 1440 (79.1) | 61 (2.7) | 1929 (97.3) | ||
| West | 183 (19.6) | 831 (80.4) | 28 (3.3) | 1157 (96.7) | ||
| Health insurance | ||||||
| Yes | 656 (19.3) | 3269 (80.7) | .649 | 109 (2.9) | 4163 (97.2) | .575 |
| No | 77 (21.3) | 240 (78.7) | 27 (3.5) | 535 (96.7) | ||
| General health | ||||||
| Excellent | 100 (19.2) | 450 (80.8) | .447 | 12 (1.8) | 537 (98.2) | .690 |
| Very good | 286 (21.4) | 1345 (78.6) | 43 (3.3) | 1663 (96.7) | ||
| Good | 237 (17.4) | 1221 (82.6) | 49 (2.9) | 1746 (97.1) | ||
| Fair | 96 (21.0) | 424 (79.0) | 25 (3.2) | 609 (96.8) | ||
| Poor | 14 (16.2) | 69 (83.8) | 7 (3.0) | 143 (97.0) | ||
| Smoking status | ||||||
| Current | 106 (20.8) | 443 (79.2) | .205 | 27 (2.6) | 666 (97.4) | .566 |
| Former | 149 (16.1) | 969 (83.9) | 23 (2.3) | 1248 (97.7) | ||
| Never | 478 (20.6) | 2097 (79.4) | 86 (3.3) | 2784 (96.7) | ||
| Diagnosed with cancer | ||||||
| Yes | 90 (17.7) | 496 (82.3) | .469 | 16 (3.9) | 646 (96.1) | .583 |
| No | 643 (19.7) | 3013 (80.3) | 120 (2.9) | 4052 (97.1) | ||
| Family history of any cancer | ||||||
| Yes | 547 (22.1) | 2454 (77.9) | <.001 | 93 (3.2) | 3230 (96.8) | .672 |
| No | 147 (14.4) | 854 (85.6) | 35 (2.3) | 1172 (97.7) | ||
| Not sure | 39 (12.4) | 201 (87.6) | 8 (3.1) | 296 (96.9) | ||
Logistic regression model
In the multivariable analysis, a lower odd of using social media for sharing health information was observed in respondents who were surveyed in year 2017 (odds ratios [ORs], 0.65; 95% confidence intervals [CIs], 0.49-0.85, P = .002), aged 50 to 64 (OR, 0.32; 95% CI, 0.24-0.43, P < .001), aged 65 and older (OR, 0.15; 95% CI, 0.09-0.25, P < .001; Table 2). The respondents’ characteristics associated with higher odds of using social media for sharing health information were female (OR, 2.22; 95% CI, 1.51-3.26, P < .001), diagnosed with cancer (OR, 1.53; 95% CI, 0.96-2.43, P = .073; reduced model [Supplemental Table 2]: OR, 1.56; 95% CI, 1.03-2.36, P = .037), or had family history of any cancer (OR, 1.56; 95% CI, 1.10-2.21, P = .014; Table 2). The results of logistic regression model with only statistically significant variables are shown in Supplementary Table 2.
Table 2.
Predictors of Use of Social Media in Health Communication Using Logistic Regression.
| Characteristics | Health Information Share | Medical Information Exchange | ||||||
|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | P Value | OR | 95% CI | P Value | |||
| Year | ||||||||
| 2013 | 1.00 | 1.00 | ||||||
| 2014 | – | – | – | 1.88 | 1.09 | 3.26 | .025 | |
| 2017 | 0.65 | 0.49 | 0.85 | .002 | – | – | – | |
| Age-group | ||||||||
| 18-49 | 1.00 | 1.00 | ||||||
| 50-64 | 0.32 | 0.24 | 0.43 | <.001 | 0.62 | 0.32 | 1.22 | .162 |
| 65+ | 0.15 | 0.09 | 0.25 | <.001 | 0.37 | 0.13 | 1.07 | .065 |
| Sex | ||||||||
| Male | 1.00 | 1.00 | ||||||
| Female | 2.22 | 1.51 | 3.26 | <.001 | 1.16 | 0.65 | 2.09 | .614 |
| Race/ethnicity | ||||||||
| Non-Hispanic white | 1.00 | 1.00 | ||||||
| Non-Hispanic black | 0.82 | 0.52 | 1.31 | .408 | 1.53 | 0.68 | 3.43 | .293 |
| Hispanic | 1.01 | 0.66 | 1.55 | .967 | 2.22 | 0.82 | 6.04 | .116 |
| Other | 1.13 | 0.69 | 1.84 | .625 | 1.97 | 0.63 | 6.14 | .235 |
| Education | ||||||||
| Less than high school | 1.00 | 1.00 | ||||||
| High school graduate | 1.48 | 0.66 | 3.34 | .338 | 1.21 | 0.12 | 12.33 | .868 |
| Some college | 2.11 | 0.89 | 4.98 | .088 | 2.11 | 0.21 | 20.94 | .517 |
| College or higher | 1.46 | 0.59 | 3.58 | .407 | 1.35 | 0.14 | 13.01 | .794 |
| Marital status | ||||||||
| Single | 1.00 | 1.00 | ||||||
| Divorced/widowed/separated | 0.97 | 0.53 | 1.80 | .930 | 1.10 | 0.40 | 3.01 | .856 |
| Married | 1.37 | 0.85 | 2.23 | .193 | 0.85 | 0.34 | 2.14 | .730 |
| Family income | ||||||||
| Less than US$20 000 | 1.00 | 1.00 | ||||||
| US$20 000 to < US$35 000 | 1.01 | 0.59 | 1.73 | .979 | 0.43 | 0.15 | 1.26 | .122 |
| US$35 000 to < US$50 000 | 1.09 | 0.60 | 1.96 | .774 | 1.37 | 0.43 | 4.44 | .589 |
| US$50 000 to < US$75 000 | 0.99 | 0.54 | 1.80 | .970 | 1.12 | 0.33 | 3.82 | .852 |
| US$75 000 or More | 0.95 | 0.54 | 1.65 | .840 | 2.20 | 0.74 | 6.57 | .154 |
| Employment | ||||||||
| Not Employed | 1.00 | 1.00 | ||||||
| Employed | 1.16 | 0.79 | 1.70 | .445 | 0.54 | 0.26 | 1.11 | .093 |
| Census region | ||||||||
| Northeast | 1.00 | 1.00 | ||||||
| Midwest | 1.43 | 0.76 | 2.70 | .258 | 1.50 | 0.52 | 4.38 | .448 |
| South | 1.47 | 0.90 | 2.39 | .118 | 1.01 | 0.39 | 2.58 | .990 |
| West | 1.24 | 0.75 | 2.06 | .394 | 1.10 | 0.40 | 2.99 | .857 |
| Health insurance | ||||||||
| No | 1.00 | 1.00 | ||||||
| Yes | 1.07 | 0.58 | 1.99 | .819 | 0.66 | 0.27 | 1.62 | .354 |
| General health | ||||||||
| Poor | 1.00 | 1.00 | ||||||
| Excellent | 0.77 | 0.22 | 2.66 | .676 | 0.54 | 0.10 | 2.94 | .466 |
| Very Good | 0.97 | 0.31 | 3.06 | .962 | 1.05 | 0.23 | 4.78 | .950 |
| Good | 0.75 | 0.23 | 2.46 | .629 | 0.91 | 0.20 | 4.07 | .898 |
| Fair | 1.13 | 0.35 | 3.62 | .839 | 0.94 | 0.20 | 4.48 | .941 |
| Smoking status | ||||||||
| Current | 1.00 | 1.00 | ||||||
| Former | 0.92 | 0.55 | 1.55 | .758 | 0.93 | 0.30 | 2.90 | .892 |
| Never | 1.05 | 0.69 | 1.61 | .813 | 1.21 | 0.56 | 2.62 | .615 |
| Diagnosed with cancer | ||||||||
| No | 1.00 | 1.00 | ||||||
| Yes | 1.53 | 0.96 | 2.43 | .073 | 1.75 | 0.49 | 6.29 | .384 |
| Family history of any cancer | ||||||||
| No | 1.00 | 1.00 | ||||||
| Yes | 1.56 | 1.10 | 2.21 | .014 | 1.59 | 0.63 | 4.01 | .319 |
| Not sure | 0.77 | 0.42 | 1.40 | .385 | 1.55 | 0.20 | 11.71 | .667 |
Abbreviations: CI, confidence interval; OR, odds ratio.
Cancer beliefs and knowledge
We observed differences in beliefs and knowledge about cancer between respondents who used social media for sharing health information and those who did not (Table 3). For instance, respondents who used social media to share health information were more concerned about the chance of getting cancer in their lifetime (P = .029), concerned about everything that causes cancer (P =.008), and worried about getting cancer (P = .004). Also, a higher proportion of respondents who used social media to share health information felt that cancer prevention is possible compared to those who did not use social media (P = .001).
Table 3.
Survey Respondents’ View on Cancer.
| Health Information Share | Medical Information Exchange | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Yes | No | P Value | Yes | No | P Value | |||||
| Cancer belief and knowledge | N | Row.% | N | Row,% | N | Row,% | N | Row,% | ||
| Likely to get cancer | .029 | .472 | ||||||||
| Very unlikely | 42 | 4.5 | 252 | 7.1 | 14 | 11.2 | 356 | 6.5 | ||
| Unlikely | 90 | 8.9 | 414 | 12.3 | 24 | 18.9 | 576 | 11.1 | ||
| Neither unlikely nor likely | 299 | 42.2 | 1479 | 42.8 | 53 | 43.4 | 1969 | 44.5 | ||
| Likely | 196 | 31.1 | 962 | 29.1 | 31 | 18.8 | 1213 | 28.4 | ||
| Very likely | 61 | 8.8 | 171 | 4.9 | 7 | 4.5 | 231 | 5.0 | ||
| Unknown | 45 | 4.5 | 231 | 3.7 | 7 | 3.2 | 353 | 4.6 | ||
| Everything causes cancer | .008 | .691 | ||||||||
| Strongly agree | 161 | 22.3 | 618 | 19.5 | 21 | 13.9 | 780 | 18.9 | ||
| Somewhat agree | 361 | 53.6 | 1613 | 47.8 | 63 | 53.1 | 2033 | 46.3 | ||
| Somewhat disagree | 139 | 16.4 | 769 | 20.8 | 27 | 15.5 | 1051 | 20.4 | ||
| Strongly disagree | 69 | 7.6 | 484 | 11.3 | 23 | 16.7 | 780 | 13.6 | ||
| Unknown | 3 | 0.1 | 25 | 0.5 | 2 | 0.7 | 54 | 0.7 | ||
| Cancer prevention not possible | .001 | .047 | ||||||||
| Strongly agree | 34 | 3.4 | 201 | 6.0 | 11 | 3.1 | 295 | 6.4 | ||
| Somewhat agree | 135 | 16.2 | 664 | 19.3 | 23 | 19.5 | 935 | 19.7 | ||
| Somewhat disagree | 307 | 46.3 | 1381 | 39.8 | 52 | 40.2 | 1810 | 40.8 | ||
| Strongly disagree | 254 | 34.0 | 1221 | 33.9 | 49 | 36.9 | 1599 | 32.4 | ||
| Unknown | 3 | 0.1 | 42 | 0.9 | 1 | 0.3 | 59 | 0.8 | ||
| Cancer more common | .446 | .639 | ||||||||
| Strongly agree | 75 | 7.4 | 292 | 7.7 | 13 | 6.1 | 433 | 8.5 | ||
| Somewhat agree | 266 | 39.4 | 1205 | 34.6 | 51 | 35.7 | 1534 | 33.0 | ||
| Somewhat disagree | 263 | 33.3 | 1343 | 39.5 | 44 | 33.0 | 1774 | 38.7 | ||
| Strongly disagree | 120 | 18.4 | 581 | 16.5 | 27 | 24.6 | 846 | 18.1 | ||
| Unknown | 9 | 1.4 | 88 | 1.7 | 1 | 0.7 | 111 | 1.7 | ||
| Frequently Worried about Cancer | .004 | .210 | ||||||||
| Not at all | 105 | 13.2 | 687 | 19.6 | 23 | 19.2 | 1021 | 21.9 | ||
| Slightly | 214 | 29.4 | 1111 | 31.2 | 40 | 26.5 | 1351 | 30.8 | ||
| Somewhat | 217 | 30.5 | 930 | 27.2 | 43 | 40.6 | 1098 | 23.5 | ||
| Moderately | 107 | 17.4 | 402 | 13.2 | 14 | 7.5 | 558 | 13.0 | ||
| Extremely | 46 | 4.9 | 176 | 5.4 | 10 | 3.0 | 337 | 6.7 | ||
| Unknown | 44 | 4.6 | 203 | 3.2 | 6 | 3.1 | 333 | 4.2 | ||
Use of Social Media to Exchange the Medical Information
Demographics and health characteristics
We examined the use of social media for exchanging medical information with a health-care professional by respondents’ demographic and health characteristics (Table 1). We observed the use of social media for exchanging medical information had almost doubled from the year 2013 (2.0%) to 2014 (3.8%; P = .025). Compared to the population aged 50 to 64 years old (2.4%) and 65 years or older (1.5%), the younger population aged 49 or less had a higher rate of social media use for exchanging medical information with a health care professional (3.5%; P = .022).
Logistic regression model
Table 2 summarizes the results from the multivariable logistic regression model that predicted the use of social media to exchange medical information with a health-care professional. Two variables, including year of survey and age group, were significantly associated with higher social media use for exchanging medical information. Respondents in the year 2014 had a higher odds of social media use for exchanging medical information compared to those in the year 2013 (OR, 1.88; 95% CI, 1.09-3.26, P = .025). A decreased odds of social media use for exchanging medical information was observed with increased age-group (50-64 years old: OR, 0.62; 95% CI, 0.32 -1.22, P = .162; 65 years or older: OR, 0.37; 95% CI, 0.13 -1.07, P = .065; reduced model [Supplemental Table 2]: OR, 0.43; 95% CI, 0.23-0.82, P = .011).
Cancer beliefs and knowledge
Respondents who used social media for exchanging medical information had higher cancer knowledge and more reasonable and optimistic views about cancer compared to respondents who did not use social media for this purpose. For instance, the respondents who used social media to exchange medical information were more likely to believe that cancer is preventable compared to individuals who do not use social media for this purpose (P = .047). These social media users were also more optimistic about the chance of getting cancer in their lifetime, were less worried about getting cancer, and were less likely to strongly agree with the statements that everything causes cancer or cancer is more common than heart disease. However, these 4 comparisons were not statistically significant (Table 3).
Discussion
Our findings from the NCI HINTS 2013, 2014, and 2017 data indicate that use of social media to share health information in social media platform has reduced significantly over time, while the use of social media to exchange medical information with a health-care professional has increased. In this study, we examined many factors associated with the use of social media for health communication, and neither disparities in minority nor low-income groups in use of social media in health communication were observed. In fact, we found that year and age are the 2 most significant factors associated with use of social media for health communication. Also, relative to respondents who did not use social media for health communication, the respondents who shared health information on social media platforms had more concerns about cancer. The respondents who used social media to exchange medical information with health-care professionals had more reasonable beliefs about cancer prevention and tended to be more accurate on cancer-related knowledge.
The findings in the opposite direction in temporal trend in the use of social media to share health information and trend to exchange medical information with a health-care professional suggest heterogeneity in health communication. The overall rate of respondents in use of social media to share the health information in 2017, 16%, has been more than doubled compared with the rate of 6% reported in a research conducted by Pew Internet & American Life survey in 2009.19 However, this rate was reduced by 36% since the year 2013. A possible explanation for this large drop after 2013 could be the raised public concern about privacy because of a number of high-profile security breaches in a number of social media platforms. Maintaining privacy on social media can be very difficult for social media users who are not familiar with complicated privacy settings from these social media platforms. After more than 3 billion user accounts has been impacted in the 2013 Yahoo data breach, data security and privacy protection have become emerging issues.20 To avoid oversharing and the risk of privacy breach, social media users may have limited the amount of health information shared on the social media platforms.
Compared to relatively less privacy education on the general social media users, health-care professionals’ participation in social media–based communication has been more guided and regulated.21 This may explain why we observed an upward trend of use of social media in medical information exchange. To date, many health-care professional societies have issued guidelines to protect patient privacy in patient–physician communication through social media platform,22-25 and this privacy-aware communication will be critical for the success in the adoption of social media in health care. As we expected, the use of social media in medical information exchange is low. There are several reasons that may explain the current low use. First, health-care professionals may hesitate to use this new media platform to communicate with their patients due to the privacy and legal concerns. Second, insurance payers do not reimburse time devoted by health-care professionals to social media–based health communication with patients. As the number of providers sharing health information and answering health questions outside the patient–physician encounters continue to increase, we will see more issues emerging beyond privacy, such as liability and quality of care. However, with more guidelines available in guiding appropriate physician–patient communication using social media, health-care professionals will feel comfortable to integrate this new tool into clinical practice.
Although the number of social media users have increased remarkably over the past 10 years, most of this growth was in the younger population. As shown in our study, the younger population still has a much higher percentage of social media use in health communication than the elderly population. As reported in the most recent data, 88% of the population aged 18 to 29 years old are social media users, compared to 78% of those aged 30 to 49 years, 64% of those aged 50 to 64 years, and 37% of those aged 65 years or older.26 Studies indicate that—other than technology difficulties—extraversion was the major characteristic predicting social media use in the elderly population.27,28 Echoed by our study, there was a very low percentage of respondents aged 65 years who used social media to share or exchange health information. Findings from the multivariable logistic regression also suggested that respondent’s age is a significant predictor of lower social media use for health communication. In the recent years, the improved access to Internet services and the relatively low cost of developing Internet-based health applications have led to increased interest in implementing social media–based health promotion and intervention programs. However, further research is needed to engage the elderly population in these health programs, especially for projects targeting age-related diseases such as various types of cancers.
Although racial and ethnic disparities in health-care access and utilization were well reported in the literature,29-32 our study found no disparities in the use of social media for health communication, including sharing health information and exchanging medical information with health-care professionals. Studies published over the past 2 decades reported racial disparities in seeking health information from the Internet among young women aged 16 to 24 years33 and in the overall population.34 However, significant efforts have been made in the past 2 decades to reduce disparities in the use of the Internet in the United States. The Federal Communications Commission’s (FCC) Universal Service program spend US$1.7 billion to make telecommunication services, including high-speed Internet, accessible to the public at reasonable and affordable costs, especially for the population living in rural and insular areas and low-income population.35 A number of initiatives from the FCC’s low-income support program, the US Agriculture Department’s Rural Utilities Service, and the US Commerce Department’s National Telecommunications and Information Administration were started in 1999 to reduce the digital divides in the United States.36 In a previous study using 2007 HINTS data, the absence of inequalities between non-white Americans and non-Hispanic white Americans in use of social media has been reported,37,38 echoing the significant improvement in reducing the access barriers in use of Internet services. Our study also demonstrated that these initiatives had improved this digital divide in social media use significantly.
More notably, the present study indicates respondents who used social media in health communication for exchanging medical information with a health-care professional may have better knowledge and rational beliefs about cancer prevention than individuals who do not use social media for this purpose. The accuracy of health information from these informal sources are always questionable. However, the newly emerged social media–based health information dissemination offered a valid venue to promote accurate health information and educational resources to the public. The social media’s inexpensive and broad reach to the public offered a large potential for the health promotion and behavior change programs.39 By following the health-care professional’s account, major news channels, medial societies’ accounts, and other health information disseminators, social media users receive most updated health information automatically. These social media users were also exposed to health information and educational resources through “share” or “retweet” from their social media network.40 However, it is important to note that being engaged in social media does not mean that social media users always receive high-quality, evidence-based, validated health information. As shown in our study, the individuals who used social media for sharing health information did not have more knowledge or more reasonable beliefs than individuals who did not share health information in social media platforms. It is critical for individuals to become connected with the right health information sources, such as information shared by health-care professionals, to obtain accurate health information that facilitates their health decision-making processes.
Although this study reported the most up-to-date trends in the use of social media for health communication, it has some limitations. First, although respondents reported the use of social media to share health information and to exchange medical information, the purpose and content of the health information communicated were unknown. Second, the survey collected self-reported data. Therefore, the responses on the use of social media depended on respondents’ recall in the past 12 months, which is subject to recall bias as well as their willingness to share this information. Third, the questions in the HINTS survey about the use of social media for health communication were not included for every cycle of the survey, so only 2 years of data were available for the analyses. Fourth, the respondents’ satisfaction with using social media in health communication was not collected. Future studies should be conducted to assess patient satisfaction with social media for health communication.
In summary, our findings suggest that use of social media for health information sharing has declined in the past few years, while the use of social media for engaging with health care professionals has increased. The fast growth of internet penetration in the United States and national initiatives in closing the digital divide will facilitate access to health information in social media platforms. The rapid expansion of social media use between patients and health-care professionals creates a unique opportunity for researchers to develop social media–based health promotion programs and potentially reduce social inequalities in health. Further research is needed to identify strategies for engaging patients in social media–based health interventions, particularly older adults, and how to promote the dissemination of current, accurate, high-quality, and evidence-based medical knowledge.
Supplemental Material
Supplementary_Files for Use of Social Media in Health Communication: Findings From the Health Information National Trends Survey 2013, 2014, and 2017 by Jinhai Huo, Raj Desai, Young-Rock Hong, Kea Turner, Arch G. Mainous and Jiang Bian in Cancer Control
Footnotes
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the University of Florida Health Cancer Center Research Pilot Grant through the Florida Consortium of National Cancer Institute Centers Program at the University of Florida (Dr. Huo and Dr. Bian. Grant Number: UFHCC TOB-SPR18-01). The content is solely the responsibility of the authors and does not necessarily represent the official views of the UF Health Cancer Center.
ORCID iD: Young-Rock Hong, MS 
https://orcid.org/0000-0002-0366-5687
Supplemental Material: Supplemental material for this article is available online.
Reference
- 1. Statista: Most famous social network sites worldwide as of July 2018, ranked by number of active users (in millions). 2018. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/. Accessed August 05, 2018.
- 2. Statista: Number of social media users worldwide from 2010 to 2021 (in billions). 2018. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/. Accessed August 05, 2018.
- 3. Social Media & User-Generated Content: United States: number of social network users 2015-2022. 2018. https://www.statista.com/statistics/278409/number-of-social-network-users-in-the-united-states/. Accessed August 28, 2018.
- 4. Social Media & User-Generated Content: Share of U.S. population with a social media profile 2008-2018. 2018. https://www.statista.com/statistics/273476/percentage-of-us-population-with-a-social-network-profile/. Accessed August 30, 2018.
- 5. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med. 2011;40(5 Suppl 2):S154–S158. [DOI] [PubMed] [Google Scholar]
- 6. Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: twitter and antibiotics. Am J Infect Control. 2010;38(3):182–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2013;15(4):e85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Sharma M, Yadav K, Yadav N, Ferdinand KC. Zika virus pandemic—analysis of Facebook as a social media health information platform. Am J Infect Control. 2017;45(3):301–302. [DOI] [PubMed] [Google Scholar]
- 9. Seeman N, Ing A, Rizo C. Assessing and responding in real time to online anti-vaccine sentiment during a flu pandemic. Healthc Q (Toronto, Ont). 2010;13 Spec No:8–15. [DOI] [PubMed] [Google Scholar]
- 10. Signorini A, Segre AM, Polgreen PM. The use of twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PloS One. 2011;6(5):e19467. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Chew C, Eysenbach G. Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak. PloS One. 2010;5(11):e14118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Khatri C, Chapman SJ, Glasbey J, et al. Social media and internet driven study recruitment: evaluating a new model for promoting collaborator engagement and participation. PloS One. 2015;10(3):e0118899. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Frandsen M, Walters J, Ferguson SG. Exploring the viability of using online social media advertising as a recruitment method for smoking cessation clinical trials. Nicotine Tob Res. 2013;16(2):247–251. [DOI] [PubMed] [Google Scholar]
- 14. Bull SS, Levine D, Schmiege S, Santelli J. Recruitment and retention of youth for research using social media: experiences from the Just/Us study. Vulnerable Children and Youth Studies. 2013;8(2):171–181. [Google Scholar]
- 15. Ramo DE, Prochaska JJ. Broad reach and targeted recruitment using Facebook for an online survey of young adult substance use. J Med Internet Res. 2012;14(1):e28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Cavallo DN, Tate DF, Ries AV, Brown JD, DeVellis RF, Ammerman AS. A social media–based physical activity intervention: a randomized controlled trial. Am J Prev Med. 2012;43(5):527–532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Bull SS, Levine DK, Black SR, Schmiege SJ, Santelli J. Social media–delivered sexual health intervention: a cluster randomized controlled trial. Am J Prev Med. 2012;43(5):467–474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Antheunis ML, Tates K, Nieboer TE. Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Educ Couns. 2013;92(3):426–431. [DOI] [PubMed] [Google Scholar]
- 19. Fox S, Jones S. Pew research center: the social life of health information. 2009. http://www.pewinternet.org/2009/06/11/the-social-life-of-health-information/. Accessed August 10, 2018.
- 20. Stempel J, Finkle J. Yahoo says all three billion accounts hacked in 2013 data theft. REUTERS. 2017. https://www.reuters.com/article/us-yahoo-cyber/yahoo-says-all-three-billion-accounts-hacked-in-2013-data-theft-idUSKCN1C82O1. Accessed December 18, 2018. [Google Scholar]
- 21. Ventola CL. Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics. 2014;39(7):491–520. [PMC free article] [PubMed] [Google Scholar]
- 22. American Society of Health-System Pharmacists. ASHP statement on use of social media by pharmacy professionals: developed through the ASHP pharmacy student forum and the ASHP section of pharmacy informatics and technology and approved by the ASHP Board of Directors on April 13, 2012, and by the ASHP House of Delegates on June 10, 2012. Am J Health Syst Pharm. 2012;69(23):2095–2097. [DOI] [PubMed] [Google Scholar]
- 23. Childs LM, Martin CY. Social media profiles: striking the right balance. Am J Health Syst Pharm. 2012;69(23):2044–2050. [DOI] [PubMed] [Google Scholar]
- 24. Fogelson NS, Rubin ZA, Ault KA. Beyond likes and tweets: an in-depth look at the physician social media landscape. Clin Obstet Gynecol. 2013;56(3):495–508. [DOI] [PubMed] [Google Scholar]
- 25. Peck JL. Social media in nursing education: responsible integration for meaningful use. J Nurs Educ. 2014;53(3):164–169. [DOI] [PubMed] [Google Scholar]
- 26. Rainie L. Americans’ complicated feelings about social media in an era of privacy concerns. 2018. http://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/. Accessed August 30, 2018.
- 27. Correa T, Hinsley AW, de Zúñiga HG. Who interacts on the Web?: the intersection of users’ personality and social media use. Comput Hum Behav. 2010;26(2):247–253. [Google Scholar]
- 28. Prensky M. Digital natives, digital immigrants part 1. On the Horizon. 2001;9(5):1–6. [Google Scholar]
- 29. Institute of Medicine Committee on Institutional Policy-Level Strategies for Increasing the Diversity of the U. S. Healthcare workforce In: Smedley BD, Stith BA, Bristow LR, eds. In the Nation’s Compelling Interest: Ensuring Diversity in the Health-Care Workforce. Washington, DC: National Academies Press (US; ); 2004. [PubMed] [Google Scholar]
- 30. United States Department of Health and Human Services HHS Action Plan to Reduce Racial and Ethnic Health Disparities: A Nation Free of Disparities in Health and Health Care. Department of Health & Human Services; 2011.
- 31. Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94(8):666. [PMC free article] [PubMed] [Google Scholar]
- 32. People H. Conclusion and future directions: CDC health disparities and inequalities report—United States, 2013. CDC Health Disparities and Inequalities Report—United States, 2013. 2013;62(3):184. [PubMed] [Google Scholar]
- 33. Laz TH, Berenson AB. Racial and ethnic disparities in internet use for seeking health information among young women. J Health Commun. 2013;18(2):250–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Lorence DP, Park H, Fox S. Racial disparities in health information access: resilience of the digital divide. J Med Syst. 2006;30(4):241–249. [DOI] [PubMed] [Google Scholar]
- 35. Federal Communications Commission Telecommunications Access Policy Division: Universal Service. 2018. https://www.fcc.gov/general/universal-service. Accessed August 15, 2018.
- 36. Government Programs Designed to Close the Divide, Fact Sheet: Government Programs Designed to Close the Divide: Falling Through the Net: Defining the Digital Divide: National Telecommunications and Information Administration (NTIA) Report; 1999.
- 37. Chou W-YS, Hunt YM, Beckjord EB, Moser RP, Hesse BW. Social media use in the united states: implications for health communication. J Med Internet Res. 2009;11(4):e48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Kontos EZ, Emmons KM, Puleo E, Viswanath K. Communication inequalities and public health implications of adult social networking site use in the United States. J Health Commun. 2010;15(Suppl 3):216–235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Korda H, Itani Z. Harnessing social media for health promotion and behavior change. Health Promotion Practice. 2013;14(1):15–23. [DOI] [PubMed] [Google Scholar]
- 40. McLean R, Richards BH, Wardman JI. The effect of Web 2.0 on the future of medical practice and education: darwikinian evolution or folksonomic revolution? Med J Australia. 2007;187(3):174. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary_Files for Use of Social Media in Health Communication: Findings From the Health Information National Trends Survey 2013, 2014, and 2017 by Jinhai Huo, Raj Desai, Young-Rock Hong, Kea Turner, Arch G. Mainous and Jiang Bian in Cancer Control