Joshua H. West

Use of social media in health promotion: purposes, key performance indicators, and evaluation metrics.

Despite the expanding use of social media, little has been published about its appropriate role in health promotion, and even less has been written about evaluation. The purpose of this article is threefold: (a) outline purposes for social media in health promotion, (b) identify potential key performance indicators associated with these purposes, and (c) propose evaluation metrics for social media related to the key performance indicators. Process evaluation is presented in this article as an overarching evaluation strategy for social media.

There’s an App for That: Content Analysis of Paid Health and Fitness Apps

Background The introduction of Apple’s iPhone provided a platform for developers to design third-party apps, which greatly expanded the functionality and utility of mobile devices for public health. Objective This study provides an overview of the developers’ written descriptions of health and fitness apps and appraises each app’s potential for influencing behavior change. Methods Data for this study came from a content analysis of health and fitness app descriptions available on iTunes during February 2011. The Health Education Curriculum Analysis Tool (HECAT) and the Precede-Proceed Model (PPM) were used as frameworks to guide the coding of 3336 paid apps. Results Compared to apps with a cost less than US

Apps of Steel: Are Exercise Apps Providing Consumers With Realistic Expectations?: A Content Analysis of Exercise Apps for Presence of Behavior Change Theory

0.99, apps exceeding US

Just a Fad? Gamification in Health and Fitness Apps

0.99 were more likely to be scored as intending to promote health or prevent disease (92.55%, 1925/3336 vs 83.59%, 1411/3336; P<.001), to be credible or trustworthy (91.11%, 1895/3336 vs 86.14%, 1454/3349; P<.001), and more likely to be used personally or recommended to a health care client (72.93%, 1517/2644 vs 66.77%, 1127/2644; P<.001). Apps related to healthy eating, physical activity, and personal health and wellness were more common than apps for substance abuse, mental and emotional health, violence prevention and safety, and sexual and reproductive health. Reinforcing apps were less common than predisposing and enabling apps. Only 1.86% (62/3336) of apps included all 3 factors (ie, predisposing, enabling, and reinforcing). Conclusions Development efforts could target public health behaviors for which few apps currently exist. Furthermore, practitioners should be cautious when promoting the use of apps as it appears most provide health-related information (predisposing) or make attempts at enabling behavior, with almost none including all theoretical factors recommended for behavior change.

Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature

Objective. To quantify the presence of health behavior theory constructs in iPhone apps targeting physical activity. Methods. This study used a content analysis of 127 apps from Apple’s (App Store) Health & Fitness category. Coders downloaded the apps and then used an established theory-based instrument to rate each app’s inclusion of theoretical constructs from prominent behavior change theories. Five common items were used to measure 20 theoretical constructs, for a total of 100 items. A theory score was calculated for each app. Multiple regression analysis was used to identify factors associated with higher theory scores. Results. Apps were generally observed to be lacking in theoretical content. Theory scores ranged from 1 to 28 on a 100-point scale. The health belief model was the most prevalent theory, accounting for 32% of all constructs. Regression analyses indicated that higher priced apps and apps that addressed a broader activity spectrum were associated with higher total theory scores. Conclusion. It is not unexpected that apps contained only minimal theoretical content, given that app developers come from a variety of backgrounds and many are not trained in the application of health behavior theory. The relationship between price and theory score corroborates research indicating that higher quality apps are more expensive. There is an opportunity for health and behavior change experts to partner with app developers to incorporate behavior change theories into the development of apps. These future collaborations between health behavior change experts and app developers could foster apps superior in both theory and programming possibly resulting in better health outcomes.

Correlates of Health-Related Social Media Use Among Adults

Background Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. Results This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification (P<.001). Behavioral theory was associated with gamification (P<.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores (P<.001), and not capacity or opportunity/trigger. Conclusions This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors.

Right Time, Right Place Health Communication on Twitter: Value and Accuracy of Location Information

Background Several thousand mobile phone apps are available to download to mobile phones for health and fitness. Mobile phones may provide a unique means of administering health interventions to populations. Objective The purpose of this systematic review was to systematically search and describe the literature on mobile apps used in health behavior interventions, describe the behavioral features and focus of health apps, and to evaluate the potential of apps to disseminate health behavior interventions. Methods We conducted a review of the literature in September 2014 using key search terms in several relevant scientific journal databases. Only English articles pertaining to health interventions using mobile phone apps were included in the final sample. Results The 24 studies identified for this review were primarily feasibility and pilot studies of mobile apps with small sample sizes. All studies were informed by behavioral theories or strategies, with self-monitoring as the most common construct. Acceptability of mobile phone apps was high among mobile phone users. Conclusions The lack of large sample studies using mobile phone apps may signal a need for additional studies on the potential use of mobile apps to assist individuals in changing their health behaviors. Of these studies, there is early evidence that apps are well received by users. Based on available research, mobile apps may be considered a feasible and acceptable means of administering health interventions, but a greater number of studies and more rigorous research and evaluations are needed to determine efficacy and establish evidence for best practices.

Use and Acceptance of Social Media Among Health Educators

Background Sixty percent of Internet users report using the Internet to look for health information. Social media sites are emerging as a potential source for online health information. However, little is known about how people use social media for such purposes. Objectives The purpose of this study was two-fold: (1) to establish the frequency of various types of online health-seeking behaviors, and (2) to identify correlates of 2 health-related online activities, social networking sites (SNS) for health-related activities and consulting online user-generated content for answers about health care providers, health facilities, or medical treatment. Methods The study consisted of a telephone survey of 1745 adults who reported going online to look for health-related information. Four subscales were created to measure use of online resources for (1) using SNS for health-related activities; (2) consulting online rankings and reviews of doctors, hospitals or medical facilities, and drugs or medical treatments; (3) posting a review online of doctors, hospitals or medical facilities, and drugs or medical treatments, and (4) posting a comment or question about health or medical issues on various social media. Univariate and multivariate logistic regression analyses were performed. Results Respondents consulted online rankings or reviews (41.15%), used SNS for health (31.58%), posted reviews (9.91%), and posted a comment, question, or information (15.19%). Respondents with a chronic disease were nearly twice as likely to consult online rankings (odds ratio [OR] 2.09, 95% CI 1.66-2.63, P<.001). Lower odds of consulting online reviews were associated with less formal education (OR 0.49, 95% CI 0.37-0.65, P<.001) and being male (OR 0.71, 95% CI 0.57-0.87, P<.001). Respondents with higher incomes were 1.5 times as likely to consult online rankings or reviews (OR 1.49, 95% CI 0.10-2.24, P=.05), than respondents with a regular provider (OR 2.05, 95% CI 1.52-2.78, P<.001), or living in an urban/suburban location (OR 1.61, 95% CI 1.17-2.22, P<.001). Older respondents were less likely to use SNS for health-related activities (OR 0.96, 95% CI 0.95-0.97, P<.001), as were males (OR 0.70, 95% CI 0.56-0.87, P<.001), whereas respondents with a regular provider had nearly twice the likelihood of using SNS for health-related activities (OR 1.89, 95% CI 1.43-2.52, P<.001). Conclusions People are using social media for seeking health information. However, individuals are more likely to consume information than they are to contribute to the dialog. The inherent value of “social” in social media is not being captured with online health information seeking. People with a regular health care provider, chronic disease, and those in younger age groups are more likely to consult online rankings and reviews and use SNS for health-related activities.

Does Proximity to Retailers Influence Alcohol and Tobacco Use Among Latino Adolescents

Background Twitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility. Objective The objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data. Methods Location data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option. Results The GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001). Conclusions Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location.

The importance of addressing social determinants of health at the local level: the case for social capital

Abstract Background: As social media use grows in popularity, health educators are challenged to think differently about how to communicate with audiences. Purpose: The purpose of this study was to explore social media use and factors that determine acceptance of social media use among health educators. Methods: A random sample of Certified Health Education Specialists (CHES) (N = 503) completed an online survey consisting of items related to the Unified Theory of Acceptance and Use of Technology (UTAUT). Results: Findings revealed that health educators most commonly used social networking sites (34.8%), podcasts (23.5%), and media sharing sites (18.5%) within their organizations. Social influence (P < 0.0001) and performance expectancy (P < 0.0001) were both positively associated with increased behavioral intentions to use social media for health promotion. Reasons for lack of use included employers monitoring or blocking social media, difficulty of use among older health educators, and the belief that social media would not enhance job performance. Discussion: Many health educators are using social media and intentions to use in practice are associated with social influence and performance expectancy. Translation to Health Education Practice: Social media use holds promise as a supporting methodology to enhance health education practice. Implementation should include attention to guidelines and best practice.