Next Article in Journal
Exploring Substance Abuse and the Dark Tetrad in Health Sciences and Non-Health Sciences Students
Previous Article in Journal
COVID-19 and the Nervous System from a Cuban Experience
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 13
Issue 9
10.3390/bs13090777
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Is There an Association between the Use of Social Media and Self-Rated Health?

by
Kholisani Ndlovu
1,
Lebogang M. Ramalepe
2,
Nwamaka C. Nwogwugwu
1 and
Bukola G. Olutola
1,*
1
School of Engineering, Science and Health, The Independent Institute of Education (IIEMSA), 144 Peter Rd, Ruimsig, Roodepoort 1724, Gauteng, South Africa
2
School of Social Science, The Independent Institute of Education (IIEMSA), 144 Peter Rd, Ruimsig, Roodepoort 1724, Gauteng, South Africa
*
Author to whom correspondence should be addressed.
Behav. Sci. 2023, 13(9), 777; https://doi.org/10.3390/bs13090777
Submission received: 30 June 2023 / Revised: 3 September 2023 / Accepted: 15 September 2023 / Published: 18 September 2023
Versions Notes

Abstract

:
This study sought to determine the association between social media and self-rated health. This study used the 2022 Health Information National Trends Survey for American adults. A statistical analysis was conducted using Chi-square and multivariable logistic regression. Of the 6018 study participants, the majority reported that they were in excellent/very good and good health (82.9%, n = 4930). More than half (58.2%, n = 3268) of the respondents reported that they visited a social media site almost every day in the past 12 months, while 76.8% (n = 4843) reported that they never interacted with people who had similar health or medical issues on social media. There was no association between everyday visits to a social media site, interaction with people with similar health/medical issues on social media, or watching a health-related video on social media and self-rated health. Those who had full-time employment were more likely to rate their health as excellent/very good/good (AOR: 2.394, 95% Conf. Int: 1.820–3.149) compared to those with no full-time employment. Marital status, confidence in taking care of oneself, education, and current smoking were associated with self-rated health. This study showed that the use of social media which included the watching of health-related videos was not associated with self-rated health.

1. Introduction

Social media is a digital system that allows users to create, share, and trade user-generated content like messages, photos, videos, and audio recordings through virtual networks, such as Facebook, Twitter, Instagram, etc. Worldwide, 2.9 billion people use Facebook as of January 2023, with India having the highest number of users, followed by the United States, Indonesia, and Brazil [1]. Furthermore, a study conducted in 2021 showed that in the United States, younger Americans aged 18–24 years use a variety of social media platforms frequently (2). Among this age group, 78% use Snapchat, 71% use Instagram, and 45% use Twitter, and the majority of adults (68%), with the exception of older adults aged 65 years and above, use Facebook [2].
Over the last decade, social media has profoundly changed the sphere of interpersonal communication, with 60% of the world’s population using social media, and the United States of America (USA) being the third largest user of social media platforms after India and China, with 90% of Americans using social media [3]. Social media’s appeal can be attributed in part to the accessibility of public message creation and distribution at a minimal cost [4].
With the ability to spread information quickly and widely, social media has been used as a tool for health objectives by providing unrestricted access, sharing and content creation of health promotion and education content, medical services, and administration [5]. This has led to a significant influence on people’s mental and physical health; therefore, it is impossible to overlook how social media affects health [6].
Social media has been integrated into health intervention to promote positive actions like physical activity and interpersonal connection, providing platforms for global peer support [5,6]. For instance, in Qatar, a weight loss campaign was successful due to social media, which was used as a tool for health behavior change motivation, progress sharing, as well as group goal achievement sharing [7]. Although social media offers numerous benefits for general health, there are also evident drawbacks such as exposure to unhealthy behaviors like tobacco use, alcohol abuse, and other behavioral issues [8].
A study conducted by Bakalu, McCloud, and Viswanath (2019) examining the association between social media use with social wellbeing, positive mental health, and self-rated health found that age, education, income, race, and ethnicity were significant predictors of self-rated health [6]. Self-rated health has been shown to be consistent with objective health status and has been used as a global measure of health status in the general population [9,10]. Additionally, various studies have shown that self-rated health is associated with mortality and morbidity, as well as disability [10]. Other associated health predictors and outcomes include physical health status, mental health status, and functional limitations [11].
The effect of social media on self-rated health is a complex issue that needs further investigation. This is particularly important given how prevalent social media platforms are in daily life and how many individuals use them for communication and self-expression. Many studies have looked at the association between general social media use and self-rated health [6,12]. However, none have looked at specific the use of social media for health reasons and its effects on self-rated health. In this study, the use of social media to share general health-related information, interact with people with similar health or medical issues on social media or online forums, and watch health-related videos on social media were used as measures of social media. Therefore, this study explored the association between these measures of social media and self-rated health.

2. Materials and Methods

Data for this cross-sectional study were obtained from the Health Information National Trends Survey 6 (HINTS 6) which was conducted from 7 March 2022 to 8 November 2022. The sampling frame for HINTS 6 consisted of a database of addresses used by Marketing Systems Group (MSG) to provide random samples of addresses. All non-vacant residential addresses in the United States present on the MSG database, including post office (P.O.) boxes, throwbacks (i.e., street addresses for which mail is redirected by the United States Postal Service to a specified P.O. box), and seasonal addresses, were subject to sampling. The sampling frame of addresses was grouped into four explicit sampling strata,
  • Addresses in urban areas with high concentrations of minority adults (HM urban);
  • Addresses in urban areas with low concentrations of minority adults (LM urban);
  • Addresses in rural areas with high concentrations of minority adults (HM rural); and
  • Addresses in rural areas with low concentrations of minority adults (LM rural).
The sampling strategy for the HINTS 6 survey consisted of a two-stage design. In the first stage, a stratified sample of addresses was selected from a file of residential addresses. In the second stage, one adult was selected within each sampled household. Of the 6505 questionnaires received, 27 were returned blank, 148 were determined to be incompletely filled out, and 78 surveys were identified as duplicates (i.e., the same household returned multiple surveys). The remaining 6252 surveys were determined to be eligible. The full methodology can be found on the HINTS website [13].

2.1. Measures

2.1.1. Self-Rated Health

Self-rated health was measured by the question “In general, would you say your health is….?” Options “Excellent”, “Very good” and “good” were categorized as good while “fair” and “poor” were categorized as “poor”.

2.1.2. Behavioral Measures

Use of Social Media

Social media was measured by asking if the respondents visited a social media site, shared general health-related information on social media, interacted with people who had similar health or medical issues on social media or online forums, and if they watched a health-related video on a social media site, all in the past 12 months. The responses to all the questions were “Almost every day”, “At least once a week”, “A few times a month”, “Less than once a month”, and “Never”. The respondents were asked about their level of confidence in finding helpful health resources on the internet. Those who said that they were completely confident or very confident or somewhat confident were categorized as confident, while those who said that they were a little confident or not confident at all were classified as not confident for the purpose of the analysis.

Use of Apps and Wearables

The respondents were asked if they had used a health or wellness app on their tablet or smartphone in the past 12 months. They were also asked if they had used an electronic wearable device or tracked their health or activity in the past 12 months. The responses were Yes/No.
Current smoking was measured by asking the participants how often they smoked with options “everyday”, “some days”, and “not at all”. Respondents were asked how confident they were about their ability to take good care of their health (confidence in self-health care). The options were “Not confident”, “a little confident”, “somewhat confident”, “Very confident”, “Completely confident”.

Socio-Demographic Measures

Age, birth gender, education, employment status, and marital status of the respondents were asked. For education, respondents were asked about the highest grade or schooling they completed. For the statistical analysis, they were classified into Grades 1–11, Grade 12, and higher than a grade 12 level of education. To measure employment status, they were asked if they usually worked 35 h or more per week in total at all jobs or businesses in the past 30 days. For the purpose of analysis, those who said “yes” were classified as those who worked 35 h or more per week in the past 30 days while those who said “no” were classified as those who worked less than 35 h per week. Marital status was classified into married, living as married, or living with a romantic partner as one category, divorced, widowed, and separated respondents were classified as another category, while those who were single and never married are one category.

2.2. Data Analysis

To account for selection probabilities and the complex sample design used in the HINTS 6, weight adjustments were made and the data analysis was performed in a survey mode using the “svy” command in STATA version 12 (Stata Corporation, College Station, TX, USA). Descriptive analyses were carried out to explore the prevalence of self-rated health and the differences between the groups were tested using Chi-square tests. Multivariable logistic regression was performed to determine the independent association between self-rated health and social media after controlling for potential confounders such as participants’ socio-economic status, current smoking, and other variables. The criterion for inclusion of the variables into the multivariable logistic model from the bivariate analysis (Chi-Square test) was set at p < 0.05. Statistical significance was also set at p < 0.05 for the regression models.

3. Results

Out of the 6252 respondents who were eligible in the HINTS 6 survey, 6018 respondents answered the question on self-rated health. More than eighty percent of them (82.9%; n = 4930) reported that they had excellent or very good or good health. A little over half of the respondents were females (50.8%; n = 3535) and between the ages 18 and 49 years (51.2%; n = 2179). Almost three quarters of them (71.5%; n = 4393) had more than a high school education. Regarding the use of social media, 58.2% (n = 3268) of the respondents visited a social media site in the 12 months preceding the survey, while only 16.2% (n = 1271) never visited any social media site. Conversely, only 1.5% (n = 77) of the respondents reported that they interacted almost every day with people who had similar health or medical issues in social media or online forums in the 12 months preceding the survey (Table 1).

3.1. Prevalence of Self-Rated Health Based on the Socio-Demographic and Behavioral Factors

In Table 2, no statistical significance was found between males and females with regard to self-rated health. Also, there was no difference in the prevalence of good self-rated health with regard to age. A higher proportion of those who were married or lived as romantic partners (86%; n = 2574) rated their health as excellent or very good or good than those who were single, never married (80.1%; n = 888), and those who were divorced or widowed or separated (75.6%; n = 1283) (p-value=0.001). More respondents who worked for 35 h or more in a week rated their health as excellent/very good/good than those who worked for less than 35 h/week (88.9% vs. 75.5%; p-value < 0.001).
For smoking status, the prevalence of self-rated health was highest among non-current smokers (84.8%) than those who smoked some days (68.4%) and those who smoked everyday (69.8%) at the time of the survey (p = 0.003). Similarly, the prevalence of excellent/very good/good self-rated health was higher among those who reported that they noticed calorie information listed next to the food on their menu at a fast food or sit-down restaurant than those who did not (85.3% vs. 80.5%, p = 0.008).

3.2. Prevalence of Excellent/Very Good/Good Self-Rated Health among Social Media Users

About 85% of the respondents who were very confident/confident in finding helpful health resources on the internet rated their health as excellent/very good/good. This was more than the prevalence of excellent/very good/good self-rated health among those who had a little confidence or no confidence in finding helpful health resources on the internet (69.2%) (p < 0.001). No significant difference was observed in the prevalence of self-rated health among the different frequencies of visits to a social media site in the 12 months preceding the survey. However, those who watched health-related videos on social media site almost everyday in the 12 months preceding the survey had the lowest prevalence of excellent/very good/good self-rated health (67%), while those who watched a health-related video on a social media site for less than a month in the 12 months preceding the survey had the highest prevalence (85.5%; n = 2058) (Table 3).

3.3. Associated Factors of Excellent/Very Good/Good Self-Rated Health

In the multivariable logistic regression, none of the social media variables were associated with self-rated health. The study respondents who worked for 35 h/week or more were more likely to rate their health as excellent/very good/good compared to those who worked for less than 35 h/week (AOR: 2.40: 95% Conf. Int: 1.82–3.15). The study respondents who were divorced or widowed or separated were less likely to have excellent/very good/good self-rated health compared to those who were married or living with a romantic partner (AOR: 0.62: 95% Conf. Int: 0.46–0.83). However, there was no significant difference between the married or those living with a romantic partner and those who were single and never married (Table 4).

4. Discussion

Our findings suggest that several factors influence self-rated health; however, social media was not one of them. Although the usage of social media sites was high, our results indicated that there were no associations between interacting with people of similar health or medical issues on social media, visiting a social media site in the past 12 months, sharing of general health-related information on social media, watching of health-related videos on social media in the 12 months preceding the survey, and self-rated health. Employment, having confidence in taking care of one’s health, smoking status, marital status, and education were the factors associated with self-rated health.
In the current study, more than 80% of the respondents visited a social media site daily to less than once a month within the last 12 months of the survey. This is in a way like what was reported by Pew Research (2021) [2], which showed that seven in ten American citizens reported that they ever used social media. Most of the current study’s respondents either watched health-related videos on social media for less than a month or never watched at all. More than a quarter never used social media as a tool for interacting with people with similar health conditions or watching health-related videos. This is particularly interesting as a previous study suggested the importance of the use of social media as a tool for connecting with people with similar health-related illnesses [6,14,15], especially post COVID-19 [16]. In addition, the benefits of watching a health-related video have been linked to health promotion [17,18], prevention [19,20], and management [21]. None of the measures of social media use in this study were associated with self-rated health. However, many studies have shown an association between the use of social media and self-rated health [6].
Social media provides social support and can help people who do not have physical support groups [22], as those who feel less connected to their family are more likely to rely more on social media for social interactions and connectedness [23]. However, Lewandowski et al., 2011, showed that individuals who indicated that the bulk of their social support came via face to-face communication reported feeling significantly better following a supportive interaction when compared with those who reported receiving support via telephone or the Internet [24].
Furthermore, in the current study, divorced/widowed/separated individuals were less likely to rate their health as excellent/very good/good compared to those who were married or living with a romantic partner. Marriage can be seen as a form of social support, and this also confirms that face-to-face support is important when it comes to one’s perception about one’s health. Many studies have shown that marriage or living with a partner is associated with good self-rated health [25,26]. Conversely, the stress of union dissolution or loss of partner may leave previously married individuals in poorer health, relative to married people [27], making them not rate their health as good. Divorce or separation can exert a significant impact on the structure of a person’s social network, making those who divorce become less popular in their social circle [28], giving rise to lack of face-to-face support or connection.
Also, widowhood can lead to low levels of bonding trust, which may eventually lead to poor self-rated health [29]. Social psychology literature indicates that the loss or lack of a significant other is associated with poor self-rated health [30,31,32]. However, the current study did not show any significant difference between self-rated health among the married and those who were single but never married. This is contrary to the findings of many studies that showed that being single is associated with having poor self-rated health [25].
We found strong evidence that confidence in taking care of oneself is associated with excellent self-rated health. This is unsurprising as several studies have reported similar findings [33,34,35,36]. Confidence in taking care of oneself is positively correlated to good/excellent self-rated health. The more confidence someone has in taking care of himself, the more the likelihood of having good self-rated health.
The results from this study suggest that full-time employment (employed for more than 35 h a week) is linked to excellent self-rated health. This is in line with several studies that have found that full-time employment is associated with high self-rated health, for example, Krokavcova et al. (2010) [37] conducted a study on patients with multiple sclerosis. Their findings suggested that employed patients with multiple sclerosis were more likely to report good self-rated health than those with part-time employment. In a recent study, An and Park (2022) [38] concluded a longitudinal study on precarious employment and self-rated health among young adults in Korea. Similarly to the findings in the current study, precarious (part-time) employment was less associated with good self-rated health compared to full time employment.
Participants who reported 12 or more years of education were more likely to have good/excellent self-rated health compared to those with less than 11 years of education. Numerous studies concur with our findings. Schellekens and Ziv (2020) analyzed the trends in education and self-rated health in the United States from 1972 to 2018. Self-rated health improved with the increase in educational attainment [39].
Participants who smoked regularly or occasionally were less likely to report excellent self-rated health than those who never smoked. This has particularly contributed to the awareness and knowledge around the health effects and hazards of using tobacco products [40,41]. Over the past two decades, more people have become conscientized and sensitized to the dangers of tobacco [42]. Thus, people who smoke are less likely to report good self-rated health.
The results of this study need to be interpretated within the limitations of the study design. This was a cross-sectional study; therefore, no causal association can be established as well as no temporal order of event. The study cannot establish that the factors shown to be associated in this study are the causes of poor or bad self-rated health. The study also relied on self-reported information, which might lead to misclassification bias. Also, the data used for the study did not have all the confounders that were used in previous studies to look at the relationship between self-rated health and social media; however, with the data, we were able to establish that there was no association between the social media measures and self-rated health. The dichotomization of the outcome variable self-rated health might have increased a Type 1 error in the study [43]. Another limitation of the study is that the study was conducted during the COVID-19 period and the pandemic might have had considerable implications for individual and collective health, as well as for emotional and social functioning. This might have a negative effect on how the participants rated their health [44]. A major strength of this study lies in the use of a large, nationally representative sample of all Americans to explore the association between self-rated health and social media.

5. Conclusions

The current study investigated the association between self-rated health and the use of social media. However, this study did not find any association between the two. The study showed that social media did not have an effect on how people feel, even when they watched health-related videos on social media. Socio-demographic factors such as employment, education, and marital status were found to be important factors of self-rated health. Others include tobacco smoking and confidence in taking care of oneself.

Author Contributions

Conceptualization, B.G.O. and K.N.; methodology, B.G.O.; validation, K.N., L.M.R., N.C.N. and B.G.O.; formal analysis, B.G.O.; investigation, K.N., L.M.R., N.C.N. and B.G.O.; data curation, B.G.O.; writing—original draft preparation, K.N., L.M.R., N.C.N. and B.G.O.; writing—review and editing, L.M.R., N.C.N. and B.G.O.; visualization, K.N., L.M.R., N.C.N. and B.G.O.; supervision, B.G.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was approved by the IIEMSA Ethics Committee. The reference number is R.15822.

Informed Consent Statement

Not applicable.

Data Availability Statement

These are publicly available data.

Acknowledgments

The authors acknowledge the National Cancer Institute, Health Information National Trends Survey for the provision of the data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Statista. Number of Social Network Users Worldwide from 2017 to 2025. Statista. Available online: https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ (accessed on 31 July 2023).
  2. Auxier, B.; Anderson, M. Social Media Use in 2021. Pew Research Center. Pew Research Center. 2021. Available online: https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2021/04/PI_2021.04.07_Social-Media-Use_FINAL.pdf (accessed on 12 June 2023).
  3. Social Media Usage in the United States—Statistics & Facts. Available online: https://www.statista.com/topics/3196/social-media-usage-in-the-united-states/#topicOverview> (accessed on 28 June 2023).
  4. Hruska, J.; Maresova, P. Use of Social Media Platforms among Adults in the United States—Behavior on Social Media. Societies 2020, 10, 27. [Google Scholar] [CrossRef]
  5. Chen, J.; Wang, Y. Social media usage for health purposes: Systematic review. J. Med. Internet Res. 2020, 23, e17917. [Google Scholar] [CrossRef] [PubMed]
  6. Bekalu, M.A.; McCloud, R.F.; Viswanath, K. Association of Social Media Use with Social Well-Being, Positive Mental Health, and Self-Rated Health: Disentangling Routine Use from Emotional Connection to Use. Health Educ. Behav. 2019, 46, 69–80. [Google Scholar] [CrossRef] [PubMed]
  7. Fernandez-Luque, L.; Singh, M.; Ofli, F.; Mejova, Y.A.; Weber, I.; Aupetit, M.; Jreige, S.K.; Elmagarmid, A.; Srivastava, J.; Ahmedna, M. Implementing 360° Quantified Self for childhood obesity: Feasibility study and experiences from a weight loss camp in Qatar. BMC Med. Inform. Decis. Mak. 2017, 17, 37. [Google Scholar] [CrossRef]
  8. Ilakkuvan, V.; Johnson, A.; Villanti, A.; Evans, D.; Turner, M. Patterns of Social Media Use and Their Relationship to Health Risks Among Young Adults. J. Adolesc. Health 2019, 64, 158–164. [Google Scholar] [CrossRef]
  9. Goldberg, P.; Gueguen, A.; Schmaus, A.; Nakache, J.P.; Goldberg, M. Longitudinal study of associations between perceived health status and self-reported diseases in the French Gazel cohort. J. Epidemiol. Community Health 2001, 55, 233–238. [Google Scholar] [CrossRef] [PubMed]
  10. Wu, S.; Wang, R.; Zhao, Y.; Ma, X.; Wu, M.; Yan, X.; He, J. The relationship between self-rated health and objective health status: A population-based study. BMC Public Health 2013, 13, 320. [Google Scholar] [CrossRef]
  11. Arnadottir, S.A.; Gunnarsdottir, E.D.; Stenlund, H.; Lundin-Olsson, L. Determinants of self-rated health in old age: A populationbased, cross-sectional study using the International Classifcation of Functioning. BMC Public Health 2011, 11, 670. [Google Scholar] [CrossRef] [PubMed]
  12. Falk Erhag, H.; Ahlner, F.; Rydberg Sterner, T.; Skoog, I.; Bergström, A. Internet use and self-rated health among Swedish 70-year-olds: A cross-sectional study. BMC Geriatr. 2019, 19, 365. [Google Scholar] [CrossRef]
  13. National Cancer Institute. Health Information National Trends Survey. Available online: https://hints.cancer.gov/ (accessed on 10 May 2023).
  14. Chung, J.E. Social networking in online support groups for health: How online social networking benefits patients. J. Health Commun. 2014, 19, 639–659. [Google Scholar] [CrossRef] [PubMed]
  15. Huang, E.; Dunbar, C.L. Connecting to patients via social media: A hype or a reality? J. Med. Mark. 2013, 13, 14–23. [Google Scholar] [CrossRef]
  16. Abbas, J.; Wang, D.; Su, Z.; Ziapour, A. The role of social media in the advent of COVID-19 pandemic: Crisis management, mental health challenges and implications. Risk Manag. 2021, 14, 1917–1932. [Google Scholar] [CrossRef] [PubMed]
  17. Yang, Q.; Wu, Z.; Xie, Y.; Xiao, X.; Wu, J.; Sang, T.; Zhang, K.; Song, H.; Wu, X.; Xu, X. The impact of health education videos on general public’s mental health and behavior during COVID-19. Glob. Health Res. Policy 2021, 6, 37. [Google Scholar] [CrossRef] [PubMed]
  18. Tuong, W.; Larsen, E.R.; Armstrong, A.W. Videos to influence: A systematic review of effectiveness of video-based education in modifying health behaviors. J. Behav. Med. 2014, 37, 218–233. [Google Scholar] [CrossRef] [PubMed]
  19. Korda, H.; Itani, Z. Harnessing social media for health promotion and behavior change. Health Promot. Pract. 2013, 14, 15–23. [Google Scholar] [CrossRef]
  20. Sarkar, U.; Le, G.M.; Lyles, C.R.; Ramo, D.; Linos, E.; Bibbins-Domingo, K. Using social media to target cancer prevention in young adults. J. Med. Internet Res. 2018, 20, e203. [Google Scholar] [CrossRef]
  21. Merolli, M.; Gray, K.; Martin-Sanchez, F. Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances. J. Biomed. Inform. 2013, 46, 957–969. [Google Scholar] [CrossRef]
  22. Han, X.; Han, W.; Qu, J.; Li, B.; Zhu, Q. What happens online stays online?—Social media dependency, online support behavior and offline effects for LGBT. Comput. Hum. Behav. 2019, 93, 91–98. [Google Scholar] [CrossRef]
  23. Winstone, L.; Mars, B.; Haworth, C.M.A.; Kidger, J. Social media use and social connectedness among adolescents in the United Kingdom: A qualitative exploration of displacement and stimulation. BMC Public Health 2021, 21, 1736. [Google Scholar] [CrossRef]
  24. Lewandowski, J.; Rosenberg, B.D.; Parks, M.J.; Siegel, J.T. The effect of informal social support: Face-to-face versus computer-mediated communication. Comput. Hum. Behav. 2011, 27, 1806–1814. [Google Scholar] [CrossRef]
  25. Lamidi, E.O. Trends in self-rated health by union status and education, 2000–2018. SSM Popul. Health 2020, 11, 100616. [Google Scholar] [CrossRef]
  26. Lindström, M. Marital status, social capital, material conditions and self-rated health: A population-based study. Health Policy 2009, 93, 172–179. [Google Scholar] [CrossRef]
  27. Hughes, M.E.; Waite, L.J. Marital biography and health at mid-life. J. Health Soc. Behav. 2009, 50, 344–358. [Google Scholar] [CrossRef] [PubMed]
  28. McDermott, R.; Fowler, J.; Christakis, N. Breaking up is Hard to Do, Unless Everyone Else is Doing it Too: Social Network Effects on Divorce in a Longitudinal Sample. Soc. Forces 2013, 92, 491–519. [Google Scholar] [CrossRef] [PubMed]
  29. Jhang, F. Changes in marital status and links to self-rated health: A study of temporal relationships. Soc. Sci. J. 2018, 55, 87–96. [Google Scholar] [CrossRef]
  30. Cundiff, J.M.; Smith, T.W.; Uchino, B.N.; Berg, C.A. Subjective social status: Construct validity and associations with psychosocial vulnerability and self-rated health. IJBM 2013, 20, 148–158. [Google Scholar] [CrossRef]
  31. Rohrer, J.E.; Bernard, M.E.; Zhang, Y.; Rasmussen, N.H.; Woroncow, H. Marital status, feeling depressed and self-rated health in rural female primary care patients. J. Eval. Clin. Pract. 2008, 14, 214–217. [Google Scholar] [CrossRef]
  32. Sudha, S.; Suchindran, C.; Mutran, E.J.; Rajan, S.I.; Sarma, P.S. Marital status, family ties, and self-rated health among elders in South India. J. Cross-Cult. Gerontol. 2006, 21, 103–120. [Google Scholar] [CrossRef]
  33. Muhammad, T.; Srivastava, S. Tooth loss and associated self-rated health and psychological and subjective wellbeing among community-dwelling older adults: A cross-sectional study in India. BMC Public Health 2022, 22, 7. [Google Scholar] [CrossRef]
  34. Kasenda, S.; Meland, E.; Hetlevik, Ø.; Mildestvedt, T.; Dullie, L. Factors associated with self-rated health in primary care in the South-Western health zone of Malawi. BMC Prim Care. 2022, 23, 88. [Google Scholar] [CrossRef]
  35. Tusa, N.; Kautiainen, H.; Elfving, P.; Sinikallio, S.; Mäntyselkä, P. Relationship between patient activation measurement and self-rated health in patients with chronic diseases. BMC Fam. Pract. 2020, 21, 225. [Google Scholar] [CrossRef] [PubMed]
  36. Zhang, Y.; Rohrer, J.; Borders, T.; Farrell, T. Patient satisfaction, self-rated health status, and health confidence: An assessment of the utility of single-item questions. Am. J. Med. Qual. 2007, 22, 42–49. [Google Scholar] [CrossRef] [PubMed]
  37. Krokavcova, M.; Nagyova, I.; Van Dijk, J.P.; Rosenberger, J.; Gavelova, M.; Middel, B.; Szilasiova, J.; Gdovinova, Z.; Groothoff, J.W. Self-rated health and employment status in patients with multiple sclerosis. Disabil. Rehabil. 2010, 32, 1742–1748. [Google Scholar] [CrossRef] [PubMed]
  38. An, S.; Park, S. Precarious Employment and Self-Rated Health in Young Adults: A Longitudinal Study from Korea. J. Occup. Environ. Med. 2022, 64, 470–475. [Google Scholar] [CrossRef] [PubMed]
  39. Schellekens, J.; Ziv, A. Education and trends in self-rated health in the United States, 1972–2018. Demogr. Res. 2020, 42, 383–398. [Google Scholar] [CrossRef]
  40. Bondurant, S.; Wallace, R.; Shetty, P.; Stratton, K. (Eds.) Clearing the Smoke: Assessing the Science Base for Tobacco Harm Reduction; National Academies Press (US): Washington, DC, USA, 2001. [Google Scholar]
  41. Warner, K.E. How to think—Not feel—About tobacco harm reduction. Nicotine Tob. Res. 2019, 21, 1299–1309. [Google Scholar] [CrossRef]
  42. Hatsukami, D.K.; Carroll, D.M. Tobacco harm reduction: Past history, current controversies and a proposed approach for the future. Prev. Med. 2020, 140, 106099. [Google Scholar] [CrossRef]
  43. Kuss, O. The danger of dichotomizing continuous variables. A visualization. Teach. Stat. 2013, 35, 78–79. [Google Scholar] [CrossRef]
  44. Pfefferbaum, B.; North, C.S. Mental health and the Covid-19 pandemic. N. Engl. J. Med. 2020, 383, 510–512. [Google Scholar] [CrossRef]
Table 1. Characteristics and demographics of the respondents.
Table 1. Characteristics and demographics of the respondents.
Characteristics % (n)
Self-rated health
Excellent/very good/good82.9 (4930)
Fair/Poor12.1 (1088)
Age of study respondents
18–3425.9 (939)
35–4925.3 (1240)
50–6427.3 (1772)
65–7413.0 (1356)
75 years and older8.6 (847)
Age of people in the household18 years and older in the household80.2 (4116)
<18 years in the household19.8 (2136)
Gender
Male49.2 (2307)
Female50.8 (3535)
Education
1–11 years of education6.9 (387)
12 years of education21.6 (1068)
>12 years of education71.5 (4393)
Employment
<35 h/week employment45.4 (3062)
≥35 h/week employment54.6 (2778)
Marital status
Married/living as married or living with a romantic partner55.9 (2997)
Divorced/Widowed/Separated12.9 (1721)
Single, never married31.2 (1119)
Confidence in self-health care
Not confident1.6 (78)
Somewhat confident/ a little confident29.7 (1657)
Completely confident/very confident68.7 (4301)
Current smoking
Not at all88.0 (5228)
Someday4.1 (195)
Everyday7.8 (449)
Notice of calories in food menu
No51.1 (3051)
Yes48.9 (2903)
Confidence in finding helpful health resources on the internet.
A little confident/Not confident at all13.3 (957)
Completely/very/somewhat confident86.7 (5163)
Health wellness app usage
No33.0 (1852)
Yes56.6 (3044)
No health apps on the phone or tablet10.5 (622)
Wearable device usage
No63.3 (4168)
Yes36.7 (2074)
Socia media site visit
Almost everyday58.2 (3268)
At least once a week11.8 (753)
A few times a month8.3 (481)
Less than a month5.4 (387)
Never16.2 (1271)
Sharing of health information on social media
Almost everyday1.2 (59)
At least once a week2.2 (134)
A few times a month7.8 (402)
Less than a month20.0 (1238)
Never69.0 (4305)
Online health interaction
Almost everyday1.5 (77)
At least once a week2.6 (136)
A few times a month6.4 (301)
Less than a month12.7 (781)
Never76.8 (4843)
Watching of health-related videos on social media
Almost everyday3.3 (171)
At least once a week7.3 (419)
A few times a month18.9 (1047)
Less than a month30.1 (1836)
Never40.4 (2685)
Table 2. Prevalence of excellent/very good/good self-rated health with regard to socio-demographic and behavioral factors.
Table 2. Prevalence of excellent/very good/good self-rated health with regard to socio-demographic and behavioral factors.
Characteristics % (n)p-Value
Age of study participants 0.134
18–3485.0 (769)
35–4984.2 (988)
50–6483.0 (1393)
65–7481.4 (1076)
75 years and above75.8 (641)
Age18 years and older in the household83.7 (3323)0.055
<18 years in the household79.8 (1607)
Gender 0.685
Male83.2 (1911)
Female82.5 (2835)
Education <0.001
1–11 years of education62.5 (230)
12 years of education79.4 (802)
>12 years of education86.3 (3719)
Employment <0.001
<35 h/week employment75.5 (2292)
≥35 h/week employment88.9 (2452)
Marital status 0.001
Married/living as married or living with a romantic partner86.0 (2574)
Divorced/Widowed/Separated75.6 (1283)
Single, never married80.1 (888)
Confidence in self-health care <0.001
Not confident27.7 (17)
Somewhat confident/ a little confident63.1 (967)
Completely confident/very confident92.8 (3935)
Current smoking 0.003
Not at all84.8 (4340)
Someday68.4 (135)
Everyday69.8 (297)
Notice of food calories in food menu 0.008
No80.5 (2368)
Yes85.3 (2469)
Table 3. Prevalence of excellent/very good/good self-rated health among social media users.
Table 3. Prevalence of excellent/very good/good self-rated health among social media users.
Characteristics % (n)p-Value
Confidence in finding helpful health resources on the internet. <0.001
A little confident/Not confident at all69.2 (638)
Completely/very/somewhat confident85.2 (4206)
Health wellness app usage 0.0261
No82.4 (1434)
Yes85.5 (2536)
No health apps on the phone or tablet78.2 (475)
Electronic wearable device usage 0.0363
No81.1 (3186)
Yes86.0 (1739)
Social media site visit 0.044
Almost everyday84.5 (2651)
At least once a week87.3 (618)
A few times a month79.3 (380)
Less than a month72.9 (291)
Never79.3 (941)
Sharing of health information on social media 0.232
Almost everyday88.6 (47)
At least once a week79.0 (96)
A few times a month76.9 (318)
Less than a month86.3 (1016)
Never82.8 (3394)
Online health interaction 0.092
Almost everyday83.6 (56)
At least once a week71.4 (91)
A few times a month72.6 (229)
Less than a month84.2 (606)
Never84.0 (3887)
Watching of health-related videos on social media. 0.017
Almost everyday67.0 (124)
At least once a week85.6 (334)
A few times a month82.3 (843)
Less than a month86.5 (1523)
Never81.4 (2058)
Table 4. Factors associated with excellent/very good/good self-rated health.
Table 4. Factors associated with excellent/very good/good self-rated health.
Characteristics Adjusted Odds Ratio (95% Confidence Interval)p-Value
Employment
<35 h/week employment1.0
≥35 h/week employment2.40 (1.82–3.15)<0.001
Confidence in self-health care
Not confident1.0
Somewhat/a little confident4.06 (1.65–9.96)0.003
Completely confident/very confident34.8 (14.9–80.9)<0.001
Current smoking
Everyday1.0
Someday1.64 (0.60–4.48)0.330
Not at all1.89 (1.24–2.89)0.004
Marital status
Married/living as married or living with a romantic partner1.0
Divorced/Widowed/Separated0.62 (0.46–0.83)0.002
Single, never married0.86 (0.63–1.18)0.345
Education
1–11 years of education1.0
12 years of education2.19 (1.39–3.45)0.001
>12 years of education2.85 (1.85–4.38)<0.001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ndlovu, K.; Ramalepe, L.M.; Nwogwugwu, N.C.; Olutola, B.G. Is There an Association between the Use of Social Media and Self-Rated Health? Behav. Sci. 2023, 13, 777. https://doi.org/10.3390/bs13090777

AMA Style

Ndlovu K, Ramalepe LM, Nwogwugwu NC, Olutola BG. Is There an Association between the Use of Social Media and Self-Rated Health? Behavioral Sciences. 2023; 13(9):777. https://doi.org/10.3390/bs13090777

Chicago/Turabian Style

Ndlovu, Kholisani, Lebogang M. Ramalepe, Nwamaka C. Nwogwugwu, and Bukola G. Olutola. 2023. "Is There an Association between the Use of Social Media and Self-Rated Health?" Behavioral Sciences 13, no. 9: 777. https://doi.org/10.3390/bs13090777

APA Style

Ndlovu, K., Ramalepe, L. M., Nwogwugwu, N. C., & Olutola, B. G. (2023). Is There an Association between the Use of Social Media and Self-Rated Health? Behavioral Sciences, 13(9), 777. https://doi.org/10.3390/bs13090777

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop