Harnessing social media for health promotion and behavior change.
TL;DR: The need for evaluating the effectiveness of various forms of social media and incorporating outcomes research and theory in the design of health promotion programs for social media is discussed.
Abstract: Rapid and innovative advances in participative Internet communications, referred to as "social media," offer opportunities for modifying health behavior. Social media let users choose to be either anonymous or identified. People of all demographics are adopting these technologies whether on their computers or through mobile devices, and they are increasingly using these social media for health-related issues. Although social media have considerable potential as tools for health promotion and education, these media, like traditional health promotion media, require careful application and may not always achieve their desired outcomes. This article summarizes current evidence and understanding of using social media for health promotion. More important, it discusses the need for evaluating the effectiveness of various forms of social media and incorporating outcomes research and theory in the design of health promotion programs for social media.
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TL;DR: Overall, SNS interventions appeared to be effective in promoting changes in health-related behaviors, and further research regarding the application of these promising tools is warranted.
487 citations
Cites background from "Harnessing social media for health ..."
...In parallel to general purpose SNSs like Facebook and Twitter, health-specific SNSs are also emerging.(6) Some are oriented towards patients with a specific chronic condition (eg, TuDiabetes), others are more general and open to patients with any chronic condition (eg, PatientsLikeMe), and a few others target people wanting to change a particular health-risk behavior (eg, smoking cessation(7)) or other health-related lifestyle factors....
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TL;DR: Being younger and possessing more education was associated with greater eHealth literacy among baby boomers and older adults and females and those highly educated, particularly at the post graduate level, reported greater use of Web 2.0 for health information.
Abstract: Background: Baby boomers and older adults, a subset of the population at high risk for chronic disease, social isolation, and poor health outcomes, are increasingly utilizing the Internet and social media (Web 2.0) to locate and evaluate health information. However, among these older populations, little is known about what factors influence their eHealth literacy and use of Web 2.0 for health information. Objective: The intent of the study was to explore the extent to which sociodemographic, social determinants, and electronic device use influences eHealth literacy and use of Web 2.0 for health information among baby boomers and older adults. Methods: A random sample of baby boomers and older adults (n=283, mean 67.46 years, SD 9.98) participated in a cross-sectional, telephone survey that included the eHealth literacy scale (eHEALS) and items from the Health Information National Trends Survey (HINTS) assessing electronic device use and use of Web 2.0 for health information. An independent samples t test compared eHealth literacy among users and non-users of Web 2.0 for health information. Multiple linear and logistic regression analyses were conducted to determine associations between sociodemographic, social determinants, and electronic device use on self-reported eHealth literacy and use of Web 2.0 for seeking and sharing health information. Results: Almost 90% of older Web 2.0 users (90/101, 89.1%) reported using popular Web 2.0 websites, such as Facebook and Twitter, to find and share health information. Respondents reporting use of Web 2.0 reported greater eHealth literacy (mean 30.38, SD 5.45, n=101) than those who did not use Web 2.0 (mean 28.31, SD 5.79, n=182), t 217.60 =−2.98, P =.003. Younger age ( b =−0.10), more education ( b =0.48), and use of more electronic devices ( b =1.26) were significantly associated with greater eHealth literacy ( R 2 =.17, R 2 adj =.14, F 9,229 =5.277, P <.001). Women were nearly three times more likely than men to use Web 2.0 for health information (OR 2.63, Wald= 8.09, df=1, P =.004). Finally, more education predicted greater use of Web 2.0 for health information, with college graduates (OR 2.57, Wald= 3.86, df =1, P =.049) and post graduates (OR 7.105, Wald= 4.278, df=1, P =.04) nearly 2 to 7 times more likely than non-high school graduates to use Web 2.0 for health information. Conclusions: Being younger and possessing more education was associated with greater eHealth literacy among baby boomers and older adults. Females and those highly educated, particularly at the post graduate level, reported greater use of Web 2.0 for health information. More in-depth surveys and interviews among more diverse groups of baby boomers and older adult populations will likely yield a better understanding regarding how current Web-based health information seeking and sharing behaviors influence health-related decision making. [J Med Internet Res 2015;17(3):e70]
484 citations
Cites background from "Harnessing social media for health ..."
...0 in health promotion [56], we also examined whether use of discrete Web 2....
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TL;DR: Anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots and a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t.
Abstract: Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation that occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts, posted between 27 April and 7 May 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t. Prior interests of disinformation adopters pinpoint to the reasons of scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with pre-existing interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots.
359 citations
Additional excerpts
...…Bastos, et al., 2014), political outreach (Bond, et al., 2012; Bakshy, et al., 2015), public health interventions (Centola, 2010; Dredze, 2012; Korda & Itani, 2013), or situational awareness (Sasaki, et al., 2010; Merchant, et al., 2011; Signorini, et al., 2011; Paul & Dredze, 2011),…...
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TL;DR: Conclusions are that social media has a future in healthcare, especially with regard to patient engagement and empowerment and community building; however, there are several challenges to overcome before the technology can achieve its potential.
Abstract: This article explores the range of social media platforms used by patients and examines the benefits and challenges of using these tools from a patient perspective. A literature review was performed to investigate the use of social media technology by patients. The MEDLINE database was searched using the terms "social media" and "patient." The search was conducted in September 2012 and yielded 765 abstracts. Initially, 63 abstracts were selected. All articles dating from 2004 through 2012 were included. Only 12 articles were found to be relevant for the purposes of the review. The results of this research found that there appears to be an increase in the use of social media by patients across the healthcare spectrum. The research indicates a promising future for the use of social media by patients; however, evidence related to the efficacy and effectiveness of social media is currently limited. Various challenges have also been identified relating to privacy and security concerns, usability, the manipulation of identity, and misinformation. The use of social media technology is an emerging trend for patients who are seeking health information. Conclusions are that such technology holds promise for improving patient engagement and empowerment and community building. Social media has a future in healthcare, especially with regard to patient engagement and empowerment; however, there are several challenges to overcome before the technology can achieve its potential.
269 citations
Cites background from "Harnessing social media for health ..."
...Studies are needed to determine how the use of social media (1) affects understanding of health and illness, (2) promotes healthy behaviors, (3) affects personal health decision making, (4) affects how patients perceive their privacy and access to their information by others, and (5) impacts the relationships of patients with their healthcare providers....
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TL;DR: An overview of the types of digital technologies used for health promotion and the socio-political implications of such use is provided, and it is contended that many digitized health promotion strategies focus on individual responsibility for health and fail to recognize the social, cultural and political dimensions of digital technology use.
Abstract: A range of digitized health promotion practices have emerged in the digital era. Some of these practices are voluntarily undertaken by people who are interested in improving their health and fitness, but many others are employed in the interests of organizations and agencies. This article provides a critical commentary on digitized health promotion. I begin with an overview of the types of digital technologies that are used for health promotion, and follow this with a discussion of the socio-political implications of such use. It is contended that many digitized health promotion strategies focus on individual responsibility for health and fail to recognize the social, cultural and political dimensions of digital technology use. The increasing blurring between voluntary health promotion practices, professional health promotion, government and corporate strategies requires acknowledgement, as does the increasing power wielded by digital media corporations over digital technologies and the data they generate. These issues provoke questions for health promotion as a practice and field of research that hitherto have been little addressed.
248 citations
Cites background from "Harnessing social media for health ..."
...…and attempt to ‘nudge’ members of target groups to change their behaviour in the interests of their health [for recent examples, see (Crutzen and De Nooijer, 2011; Kratzke and Cox, 2012; Buhi et al., 2013; Chou et al., 2013; Korda and Itani, 2013; Epton et al., 2014; Smith et al., 2014)]....
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...After realizing the reach and potential impact of these technologies, health promoters have experimented with using text messages, social media sites and apps to strategically disseminate information about preventive health, collect data about people’s health-related behaviours and attempt to ‘nudge’ members of target groups to change their behaviour in the interests of their health [for recent examples, see (Crutzen and De Nooijer, 2011; Kratzke and Cox, 2012; Buhi et al., 2013; Chou et al., 2013; Korda and Itani, 2013; Epton et al., 2014; Smith et al., 2014)]....
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References
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TL;DR: The present review provides a framework for the development of a science of Internet-based interventions and provides a rationale for investing in more intensive theory- based interventions that incorporate multiple behavior change techniques and modes of delivery.
Abstract: Background: The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked these to effectiveness. Objectives: The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also to link different modes to effect sizes. Methods: We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken after the intervention. Results: We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On average, interventions had a statistically small but significant effect on health-related behavior (d+ = 0.16, 95% CI 0.09-0.23). More extensive use of theory was associated with increases in effect size (P = .049), and, in particular, interventions based on the theory of planned behavior tended to have substantial effects on behavior (d+ = 0.36, 95% CI 0.15-0.56). Interventions that incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated fewer techniques (P < .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional methods of communicating with participants, especially the use of short message service (SMS), or text, messages. Conclusions: The review provides a framework for the development of a science of Internet-based interventions, and our findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change techniques and modes of delivery. [J Med Internet Res 2010;12(1):e4]
2,224 citations
"Harnessing social media for health ..." refers background in this paper
...22 HEALTH PROMOTION PRACTICE / January 2013 mixed results, but specific applications have demonstrated great promise and effect (Webb et al., 2010)....
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...One of the most comprehensive investigations we identified is a recent meta-analysis of 85 studies by Webb et al. (2010) that found interventions that were strongly based in theory had greater impact than those that were not, and interventions that incorporated more behavior change techniques…...
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...Evaluation of these interventions shows 22 HEALTH PROMOTION PRACTICE / January 2013 mixed results, but specific applications have demonstrated great promise and effect (Webb et al., 2010)....
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...Theory-based interventions: Although there is considerable variation in the efficacy of individual interventions, evidence shows that those developed and guided by theories of social and behavioral change are more effective at promoting desired change than efforts that are not theory based (Webb et al., 2010)....
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...Webb et al. (2010) found that tailored text messages are highly effective to promote interaction with the intervention, to send motivational messages (e.g., reminders of the benefits of exercise), to challenge dysfunctional beliefs, or to provide a cue to action....
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TL;DR: The need for a “science of attrition” is argued, that is, a need to develop models for discontinuation of e health applications and the related phenomenon of participants dropping out of eHealth trials, as well as measures to be reported including the relative risk of dropping out or of stopping the use of an application.
Abstract: In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.
[J Med Internet Res 2005;7(1):e11]
2,083 citations
"Harnessing social media for health ..." refers background in this paper
...Attrition, when participants stop usage or are lost to follow-up, has also been identified as a fundamental issue for evaluation of online interventions (Eysenbach, 2005)....
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TL;DR: Recent growth of social media is not uniformly distributed across age groups; therefore, health communication programs utilizing social media must first consider the age of the targeted population to help ensure that messages reach the intended audience.
Abstract: Background: Given the rapid changes in the communication landscape brought about by participative Internet use and social media, it is important to develop a better understanding of these technologies and their impact on health communication. The first step in this effort is to identify the characteristics of current social media users. Up-to-date reporting of current social media use will help monitor the growth of social media and inform health promotion/communication efforts aiming to effectively utilize social media. Objective: The purpose of the study is to identify the sociodemographic and health-related factors associated with current adult social media users in the United States. Methods: Data came from the 2007 iteration of the Health Information National Trends Study (HINTS, N = 7674). HINTS is a nationally representative cross-sectional survey on health-related communication trends and practices. Survey respondents who reported having accessed the Internet (N = 5078) were asked whether, over the past year, they had (1) participated in an online support group, (2) written in a blog, (3) visited a social networking site. Bivariate and multivariate logistic regression analyses were conducted to identify predictors of each type of social media use. Results: Approximately 69% of US adults reported having access to the Internet in 2007. Among Internet users, 5% participated in an online support group, 7% reported blogging, and 23% used a social networking site. Multivariate analysis found that younger age was the only significant predictor of blogging and social networking site participation; a statistically significant linear relationship was observed, with younger categories reporting more frequent use. Younger age, poorer subjective health, and a personal cancer experience predicted support group participation. In general, social media are penetrating the US population independent of education, race/ethnicity, or health care access. Conclusions: Recent growth of social media is not uniformly distributed across age groups; therefore, health communication programs utilizing social media must first consider the age of the targeted population to help ensure that messages reach the intended audience. While racial/ethnic and health status–related disparities exist in Internet access, among those with Internet access, these characteristics do not affect social media use. This finding suggests that the new technologies, represented by social media, may be changing the communication pattern throughout the United States. [J Med Internet Res 2009;11(4):e48]
964 citations
"Harnessing social media for health ..." refers background in this paper
...Chou et al. (2009) found that people of any ethnicity, regardless of education level, used social networking sites at a higher rate than all non-Hispanic Whites, noting that these differences are likely explained by age....
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TL;DR: An improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables showed an improvement in outcome size comparisons.
Abstract: BACKGROUND: A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed.
OBJECTIVE: This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions.
METHODS: The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies.
RESULTS: Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001).
CONCLUSIONS: The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
[J Med Internet Res 2004;6(4):e40]
947 citations
TL;DR: IHCAs were found to have a negative effect on clinical outcomes, and consumers who wish to increase their knowledge or social support amongst people with a similar problem may find an IHCA helpful, however, consumers whose primary aim is to achieve optimal clinical outcomes should not use an IhCA at present.
Abstract: BACKGROUND: Interactive Health Communication Applications (IHCAs) are computer-based, usually web-based health information packages for patients that combine information with at least one of social support, decision support, or behaviour change support. These are innovations in health care and their effects on health are uncertain. OBJECTIVES: To assess the effects of IHCAs for people with chronic disease. SEARCH STRATEGY: We designed a four-part search strategy. First, we searched electronic bibliographic databases for published work; second, we searched the grey literature and third, we searched for ongoing and recently completed clinical trials in the appropriate databases. Finally, researchers of included studies were contacted, and reference lists from relevant primary and review articles were followed up. As IHCAs require relatively new technology, the search commenced at 1990 where possible. SELECTION CRITERIA: Randomised controlled trials (RCTs) of Interactive Health Communication Applications for adults and children with chronic disease. DATA COLLECTION AND ANALYSIS: One reviewer screened abstracts. Two reviewers screened all candidate studies to determine eligibility, apply quality criteria, and extract data from included studies. Authors of included RCTs were contacted for missing data. Results of RCTs were pooled using a random effects model and standardised mean differences (SMDs) were calculated to provide net effect sizes. MAIN RESULTS: We screened 24,757 unique citations and retrieved 958 papers for further assessment, yielding 28 RCTs involving 4042 participants. One of these had an inadequate method of concealment of allocation, and sensitivity analyses were performed to determine the effects of including or excluding these data in the meta-analyses. Results in the abstract are from the meta-analyses excluding data from this study.IHCAs were found to have a positive effect on knowledge (SMD 0.49; 95% confidence interval (CI) 0.14 to 0.84) and on social support (SMD 0.47; 95% CI 0.28 to 0.66). IHCAs were found to have no effect on self-efficacy (SMD 0.15; 95% CI -0.13 to 0.43) or behavioural outcomes (SMD -0.09; 95% CI -0.49 to 0.32). IHCAs had a negative effect on clinical outcomes (SMD -0.32; 95% CI -0.63 to -0.02). REVIEWERS' CONCLUSIONS: The number and range of IHCAs is increasing rapidly; however there is a shortage of high quality evaluative data. Consumers who wish to increase their knowledge or social support amongst people with a similar problem may find an IHCA helpful. However, consumers whose primary aim is to achieve optimal clinical outcomes should not use an IHCA at present. Further research is needed to determine the reason for this negative effect on clinical outcomes, whether an optimal IHCA can achieve behaviour change and improved health outcomes, and if so, what are the essential features of such an IHCA, and the extent to which they differ according to patient group or condition.
753 citations