Diffusion of an evidence-based smoking cessation intervention through Facebook: a randomised controlled trial study protocol

A Proof-of-Concept Pilot Test of a Behavioral Intervention to Improve Adherence to Dietary Recommendations for Cancer Prevention

OBJECTIVES

Prevention programs that can help adults improve the quality of their diets to reduce cancer risk are needed. This Phase IIa study prospectively tested a mHealth intervention designed to improve adherence to dietary quality guidelines for cancer prevention.

METHODS

All participants (N = 62) received nutrition education and a self-regulation skills curriculum, with a primary target of changing grocery shopping behavior. Using a randomized, factorial design, the study varied whether each of the following 4 components were added to the 20-week intervention: (1) location-triggered app messaging, delivered when individuals arrived at grocery stores, (2) reflections on benefits of change, delivered with extra coaching time and tailored app messages, (3) coach monitoring, in which food purchases were digitally monitored by a coach, and (4) involvement of a household member in the intervention.

RESULTS

Benchmarks were successfully met for recruitment, retention, and treatment acceptability. Across conditions, there were significant reductions in highly processed food intake (P < .001, η2 = .48), red and processed meat intake (P < .001, η2 = .20), and sugar-sweetened beverage intake (P = .008, η2 = .13) from pre-to post-treatment. Analyses examining whether each intervention component influenced change across time found that participants who received coach monitoring increased their intake of fruits, vegetables, and fiber, whereas those with no coach monitoring had less improvement (P = .01, η2 = .14). The improvement in red and processed meat was stronger among participants with household support ON, at a marginally significant level, than those with household support OFF (P = .056, η2 = .07).

CONCLUSION

This study showed feasibility, acceptability, and preliminary signals of efficacy of a remotely delivered intervention to facilitate adherence to dietary guidelines for cancer prevention and that coach monitoring and household support may be especially effective strategies. A fully powered clinical trial is warranted to test an optimized version of the intervention that includes nutrition education, self-regulation skills training, coach monitoring, and household member involvement.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04947150.

Evaluating the Efficacy of a Social Media-Based Intervention (Warna-Warni Waktu) to Improve Body Image Among Young Indonesian Women: Parallel Randomized Controlled Trial (Preprint)

BACKGROUND

Body dissatisfaction is a global issue, particularly among adolescent girls and young women. Effective body image interventions exist but face barriers to scaling up, particularly in lower- and middle-income countries, such as Indonesia, where a need exists.

OBJECTIVE

We aimed to evaluate the acceptability and efficacy of Warna-Warni Waktu, a social media-based, fictional 6-episode video series with self-guided web-based activities for improving body image among young Indonesian adolescent girls and young women. We hypothesized that Warna-Warni Waktu would increase trait body satisfaction and mood and decrease internalization of appearance ideals and skin shade dissatisfaction relative to the waitlist control condition. We also anticipated improvements in state body satisfaction and mood immediately following each video.

METHODS

We conducted a web-based, 2-arm randomized controlled trial among 2000 adolescent girls and young women, aged 15 to 19 years, recruited via telephone by an Indonesian research agency. Block randomization (1:1 allocation) was performed. Participants and researchers were not concealed from the randomized arm. Participants completed self-report assessments of trait body satisfaction (primary outcome) and the internalization of appearance ideals, mood, and skin shade dissatisfaction at baseline (before randomization), time 2 (1 day after the intervention [T2]), and time 3 (1 month after the intervention [T3]). Participants also completed state body satisfaction and mood measures immediately before and after each video. Data were evaluated using linear mixed models with an intent-to-treat analysis. Intervention adherence was tracked. Acceptability data were collected.

RESULTS

There were 1847 participants. Relative to the control condition (n=923), the intervention group (n=924) showed reduced internalization of appearance ideals at T2 (F_1,1758=40.56, P<.001, partial η²=0.022) and T3 (F_1,1782=54.03, P<.001, partial η²=0.03) and reduced skin shade dissatisfaction at T2 (F_1,1744=8.05, P=.005, partial η²=0.005). Trait body satisfaction improvements occurred in the intervention group at T3 (F_{1, 1781}=9.02, P=.005, partial η²=0.005), which was completely mediated by the internalization change scores between baseline and T2 (indirect effect: β=.03, 95% CI 0.017-0.041; direct effect: β=.03, P=.13), consistent with the Tripartite Influence Model of body dissatisfaction. Trait mood showed no significant effects. Dependent sample t tests (2-tailed) found each video improved state body satisfaction and mood. Cumulative analyses found significant and progressive improvements in pre- and poststate body satisfaction and mood. Intervention adherence was good; participants watched an average of 5.2 (SD 1.66) videos. Acceptability scores were high for understandability, enjoyment, age appropriateness, usefulness, and likelihood to recommend.

CONCLUSIONS

Warna-Warni Waktu is an effective eHealth intervention to reduce body dissatisfaction among Indonesian adolescent girls and young women. Although the effects were small, Warna-Warni Waktu is a scalable, cost-effective alternative to more intense interventions. Initially, dissemination through paid social media advertising will reach thousands of young Indonesian women.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05383807, https://clinicaltrials.gov/ct2/show/NCT05383807 ; ISRCTN Registry ISRCTN35483207, https://www.isrctn.com/ISRCTN35483207.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/33596.

Decision fusion in healthcare and medicine: a narrative review

OBJECTIVE

To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.

BACKGROUND

The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.

METHODS

We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.

CONCLUSIONS

Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

An exploration of the Facebook social networks of smokers and non-smokers

BACKGROUND

Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use.

OBJECTIVES

These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook.

METHODS

During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates.

RESULTS

The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers.

CONCLUSIONS

This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants.

Innovative approaches to support smoking cessation for individuals with mental illness and co-occurring substance use disorders

INTRODUCTION

Tobacco remains the leading preventable cause of death in the US, accounting for over 520,000 deaths annually. While the smoking prevalence has declined over the past 50 years, those with mental illness and addictive disorders continue to smoke at high levels and with significant tobacco-related health problems. Areas covered: This review highlights the epidemiology, contributing factors, and evidence-base for intervening upon tobacco use in those with mental illness and addictive disorders. Historically underprioritized, a growing body of literature supports treating tobacco within mental health and addiction treatment settings. Critically, treating tobacco use appears to support, and not harm, mental health recovery and sobriety. This review also summarizes novel, emerging approaches to mitigate the harms of cigarette smoking. Expert commentary: People with mental illness and addictive disorders have a high prevalence of tobacco use with serious health harms. Treating tobacco use is essential. Evidence-based strategies include individual treatments that are stage-matched to readiness to quit and combine cessation medications with behavioral therapies, supported by smoke-free policies in treatment settings and residential environments. Emerging approaches, with a focus on harm reduction, are electronic nicotine delivery systems and tobacco regulatory efforts to reduce the nicotine content in cigarettes, thereby reducing their addiction potential.

Systematic review of social media interventions for smoking cessation

BACKGROUND

Popular social media could extend the reach of smoking cessation efforts. In this systematic review, our objectives were: 1) to determine whether social media interventions for smoking cessation are feasible, acceptable, and potentially effective; 2) to identify approaches for recruiting subjects; and 3) to examine the specific intervention design components and strategies employed to promote user engagement and retention.

METHODS

We searched Scopus, Medline, EMBASE, Cochrane Central, PsychINFO, CINAHL, and Web of Science through July 2016 and reference lists of relevant articles. Included studies described social media interventions for smoking cessation and must have reported outcomes related to feasibility, acceptability, usability, or smoking-related outcomes.

RESULTS

We identified 7 studies (all were published since 2014) that enrolled 9755 participants (median=136 [range 40 to 9042]). Studies mainly used Facebook (n=4) or Twitter (n=2), and emerged as feasible and acceptable. Five studies reported smoking-related outcomes such as greater abstinence, reduction in relapse, and an increase in quit attempts. Most studies (n=6) recruited participants using online or Facebook advertisements. Tailored content, targeted reminders, and moderated discussions were used to promote participant engagement. Three studies found that active participation through posting comments or liking content may be associated with improved outcomes. Retention ranged from 35% to 84% (median=70%) across the included studies.

CONCLUSIONS

Our review highlights the feasibility, acceptability and preliminary effectiveness of social media interventions for smoking cessation. Future research should continue to explore approaches for promoting user engagement and retention, and whether sustained engagement translates to clinically meaningful smoking cessation outcomes.

Internet-based interventions for smoking cessation

BACKGROUND

Tobacco use is estimated to kill 7 million people a year. Nicotine is highly addictive, but surveys indicate that almost 70% of US and UK smokers would like to stop smoking. Although many smokers attempt to give up on their own, advice from a health professional increases the chances of quitting. As of 2016 there were 3.5 billion Internet users worldwide, making the Internet a potential platform to help people quit smoking.

OBJECTIVES

To determine the effectiveness of Internet-based interventions for smoking cessation, whether intervention effectiveness is altered by tailoring or interactive features, and if there is a difference in effectiveness between adolescents, young adults, and adults.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, which included searches of MEDLINE, Embase and PsycINFO (through OVID). There were no restrictions placed on language, publication status or publication date. The most recent search was conducted in August 2016.

SELECTION CRITERIA

We included randomised controlled trials (RCTs). Participants were people who smoked, with no exclusions based on age, gender, ethnicity, language or health status. Any type of Internet intervention was eligible. The comparison condition could be a no-intervention control, a different Internet intervention, or a non-Internet intervention. To be included, studies must have measured smoking cessation at four weeks or longer.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed and extracted data. We extracted and, where appropriate, pooled smoking cessation outcomes of six-month follow-up or more, reporting short-term outcomes narratively where longer-term outcomes were not available. We reported study effects as a risk ratio (RR) with a 95% confidence interval (CI).We grouped studies according to whether they (1) compared an Internet intervention with a non-active control arm (e.g. printed self-help guides), (2) compared an Internet intervention with an active control arm (e.g. face-to-face counselling), (3) evaluated the addition of behavioural support to an Internet programme, or (4) compared one Internet intervention with another. Where appropriate we grouped studies by age.

MAIN RESULTS

We identified 67 RCTs, including data from over 110,000 participants. We pooled data from 35,969 participants.There were only four RCTs conducted in adolescence or young adults that were eligible for meta-analysis.Results for trials in adults: Eight trials compared a tailored and interactive Internet intervention to a non-active control. Pooled results demonstrated an effect in favour of the intervention (RR 1.15, 95% CI 1.01 to 1.30, n = 6786). However, statistical heterogeneity was high (I² = 58%) and was unexplained, and the overall quality of evidence was low according to GRADE. Five trials compared an Internet intervention to an active control. The pooled effect estimate favoured the control group, but crossed the null (RR 0.92, 95% CI 0.78 to 1.09, n = 3806, I² = 0%); GRADE quality rating was moderate. Five studies evaluated an Internet programme plus behavioural support compared to a non-active control (n = 2334). Pooled, these studies indicated a positive effect of the intervention (RR 1.69, 95% CI 1.30 to 2.18). Although statistical heterogeneity was substantial (I² = 60%) and was unexplained, the GRADE rating was moderate. Four studies evaluated the Internet plus behavioural support compared to active control. None of the studies detected a difference between trial arms (RR 1.00, 95% CI 0.84 to 1.18, n = 2769, I² = 0%); GRADE rating was moderate. Seven studies compared an interactive or tailored Internet intervention, or both, to an Internet intervention that was not tailored/interactive. Pooled results favoured the interactive or tailored programme, but the estimate crossed the null (RR 1.10, 95% CI 0.99 to 1.22, n = 14,623, I² = 0%); GRADE rating was moderate. Three studies compared tailored with non-tailored Internet-based messages, compared to non-tailored messages. The tailored messages produced higher cessation rates compared to control, but the estimate was not precise (RR 1.17, 95% CI 0.97 to 1.41, n = 4040), and there was evidence of unexplained substantial statistical heterogeneity (I² = 57%); GRADE rating was low.Results should be interpreted with caution as we judged some of the included studies to be at high risk of bias.

AUTHORS' CONCLUSIONS

The evidence from trials in adults suggests that interactive and tailored Internet-based interventions with or without additional behavioural support are moderately more effective than non-active controls at six months or longer, but there was no evidence that these interventions were better than other active smoking treatments. However some of the studies were at high risk of bias, and there was evidence of substantial statistical heterogeneity. Treatment effectiveness in younger people is unknown.

Internet as a Source of Long-Term and Real-Time Professional, Psychological, and Nutritional Treatment: A Qualitative Case Study Among Former Israeli Soviet Union Immigrants

Background

The Internet is considered to be an effective source of health information and consultation for immigrants. Nutritional interventions for immigrants have become increasingly common over the past few decades. However, each population of immigrants has specific needs. Understanding the factors influencing the success of nutrition programs among immigrants requires an examination of their attitudes and perceptions, as well as their cultural values.

Objective

The purpose of this study was to examine perceptions of the Internet as a tool for long-term and “real-time” professional, psychological, and nutritional treatment for immigrants from the former Soviet Union who immigrated to Israel (IIFSU) from 1990 to 2012.

Methods

A sample of nutrition forum users (n=18) was interviewed and comments of 80 users were analyzed qualitatively in accordance with the grounded theory principles.

Results

The results show that IIFSU perceive the Internet as a platform for long-term and “real-time” dietary treatment and not just as an informative tool. IIFSU report benefits of online psychological support with professional dietary treatment. They attribute importance to cultural customization, which helps reduce barriers to intervention.

Conclusions

In light of the results, when formulating nutritional programs, it is essential to have a specific understanding of immigrants’ cultural characteristics and their patterns of Internet use concerning dietary care.

You Get What You Give: Understanding Reply Reciprocity and Social Capital in Online Health Support Forums

Adopting a social network analysis approach, the present study examined social capital and network dynamics of online support seeking and support provision in a depression forum. We constructed a depression forum network by mapping out all of the users and the reply ties among them. The findings showed a consistently reciprocal pattern between users' replies sent to others and replies received from others. Forum users' bridging social capital was positively associated with the source diversity of their received replies and negatively associated with the average length of their received replies. Furthermore, forum users' bonding social capital was positively associated with the average length of their received replies and negatively associated with the source diversity of their received replies.

An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities

The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

Online tobacco websites and online communities-who uses them and do users quit smoking? The quit-primo and national dental practice-based research network Hi-Quit studies

Online tobacco cessation communities are beneficial but underused. Our study examined whether, among smokers participating in a web-assisted tobacco intervention (Decide2quit.org), specific characteristics were associated with navigating to BecomeAnEx.org, an online cessation community, and with subsequent quit rates. Among smokers (N = 759) registered with Decide2quit.org, we identified visitors to BecomeAnEx.org, examining associations between smoker characteristics and likelihood of visiting. We then tested for associations between visits and 6-month cessation (point prevalence). We also tested for an interaction between use of other online support-seeking (Decide2quit.org tobacco cessation coaches), visiting, and 6-month cessation. One quarter (26.0 %; n = 197) of the smokers visited BecomeAnEx.org; less than one tenth (7.5 %; n = 57) registered to participate in the online forum. Visitors were more likely to be female (73.0 vs. 62.6 % of non-visitors, P < 0.01) to have visited a cessation website before (33.0 vs. 17.4 %, P < 0.01) and to report quit attempts in the previous year (62.0 vs. 53.0 %, P = 0.03). In analyses of all participants, BecomeAnEx.org visiting was not associated with 6-month quit completion. Among participants who communicated with a coach, BecomeAnEx.org visiting also lacked a significant association with 6 month quit completion, although a non-significant trend toward quit completion in visitors was noted (OR 2.21, 95 % CI 0.81-3.1). Online cessation communities attract smokers with previous cessation website experience and recent quit attempts. Community visiting was not associated with quit rates in our study, but low use may have limited our power to detect differences. Further research should explore whether an additive effect can be achieved by offering community visitors support via online coaches.

Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives

Background

Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used.

Objective

The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools.

Methods

We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important.

Results

Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking.

Conclusions

Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation.

Update on Smoking Cessation: E-Cigarettes, Emerging Tobacco Products Trends, and New Technology-Based Interventions

Tobacco use disorders (TUDs) continue to be overly represented in patients treated in mental health and addiction treatment settings. It is the most common substance use disorder (SUD) and the leading cause of health disparities and increased morbidity/mortality amongst individuals with a psychiatric disorder. There are seven Food and Drug Administration (FDA) approved medications and excellent evidence-based psychosocial treatment interventions to use in TUD treatment. In the past few years, access to and use of other tobacco or nicotine emerging products are on the rise, including the highly publicized electronic cigarette (e-cigarette). There has also been a proliferation of technology-based interventions to support standard TUD treatment, including mobile apps and web-based interventions. These tools are easily accessed 24/7 to support outpatient treatment. This update will review the emerging products and counter-measure intervention technologies, including how clinicians can integrate these tools and other community-based resources into their practice.

Diffusion of an Evidence-Based Smoking Cessation Intervention Through Facebook: A Randomized Controlled Trial

OBJECTIVES

To examine the diffusion of an evidence-based smoking cessation application ("app") through Facebook social networks and identify specific intervention components that accelerate diffusion.

METHODS

Between December 2012 and October 2013, we recruited adult US smokers ("seeds") via Facebook advertising and randomized them to 1 of 12 app variants using a factorial design. App variants targeted components of diffusion: duration of use (t), "contagiousness" (β), and number of contacts (Z). The primary outcome was the reproductive ratio (R), defined as the number of individuals installing the app ("descendants") divided by the number of a seed participant's Facebook friends.

RESULTS

We randomized 9042 smokers. App utilization metrics demonstrated between-variant differences in expected directions. The highest level of diffusion (R = 0.087) occurred when we combined active contagion strategies with strategies to increase duration of use (incidence rate ratio = 9.99; 95% confidence interval = 5.58, 17.91; P < .001). Involving nonsmokers did not affect diffusion.

CONCLUSIONS

The maximal R value (0.087) is sufficient to increase the numbers of individuals receiving treatment if applied on a large scale. Online interventions can be designed a priori to spread through social networks.

Effects of Social Network Exposure on Nutritional Learning: Development of an Online Educational Platform

Background

Social networking sites (SNSs) such as Facebook have the potential to enhance online public health interventions, in part, as they provide social exposure and reinforcement.

Objective

The objective of the study was to evaluate whether social exposure provided by SNSs enhances the effects of online public health interventions.

Methods

As a sample intervention, we developed Food Hero, an online platform for nutritional education in which players feed a virtual character according to their own nutritional needs and complete a set of virtual sport challenges. The platform was developed in 2 versions: a "private version" in which a user can see only his or her own score, and a "social version" in which a user can see other players’ scores, including preexisting Facebook friends. We assessed changes in participants’ nutritional knowledge using 4 quiz scores and 3 menu-assembly scores. Monitoring feeding and exercising attempts assessed engagement with the platform.

Results

The 2 versions of the platform were randomly assigned between a study group (30 members receiving the social version) and a control group (33 members, private version). The study group's performance on the quizzes gradually increased over time, relative to that of the control group, becoming significantly higher by the fourth quiz (P=.02). Furthermore, the study group's menu-assembly scores improved over time compared to the first score, whereas the control group's performance deteriorated. Study group members spent an average of 3:40 minutes assembling each menu compared to 2:50 minutes in the control group, and performed an average of 1.58 daily sport challenges, compared to 1.21 in the control group (P=.03).

Conclusions

This work focused on isolating the SNSs' social effects in order to help guide future online interventions. Our results indicate that the social exposure provided by SNSs is associated with increased engagement and learning in an online nutritional educational platform.

The Past, Present, and Future of Nicotine Addiction Therapy

The tobacco addiction treatment field is progressing through innovations in medication development, a focus on precision medicine, and application of new technologies for delivering support in real time and over time. This article reviews the evidence for combined and extended cessation pharmacotherapy and behavioral strategies including provider advice, individual counseling, group programs, the national quitline, websites and social media, and incentives. Healthcare policies are changing to offer cessation treatment to the broad population of smokers. With knowledge of the past and present, this review anticipates what is likely on the horizon in the clinical and public health effort to address tobacco addiction.

Impact of Baseline Assessment Modality on Enrollment and Retention in a Facebook Smoking Cessation Study

Background

Few studies have addressed enrollment and retention methods in online smoking cessation interventions. Fully automated Web-based trials can yield large numbers of participants rapidly but suffer from high rates of attrition. Personal contact with participants can increase recruitment of smokers into cessation trials and improve participant retention.

Objective

To compare the impact of Web-based (WEB) and phone (PH) baseline assessments on enrollment and retention metrics in the context of a Facebook smoking cessation study.

Methods

Participants were recruited via Facebook and Google ads which were randomly displayed to adult smokers in the United States over 27 days from August to September 2013. On each platform, two identical ads were randomly displayed to users who fit the advertising parameters. Clicking on one of the ads resulted in randomization to WEB, and clicking on the other ad resulted in randomization to PH. Following online eligibility screening and informed consent, participants in the WEB arm completed the baseline survey online whereas PH participants completed the baseline survey by phone with a research assistant. All participants were contacted at 30 days to complete a follow-up survey that assessed use of the cessation intervention and smoking outcomes. Participants were paid $15 for follow-up survey completion.

Results

A total of 4445 people clicked on the WEB ad and 4001 clicked on the PH ad: 12.04% (n=535) of WEB participants and 8.30% (n=332) of PH participants accepted the online study invitation (P<.001). Among the 726 participants who completed online eligibility screening, an equivalent proportion in both arms was eligible and an equivalent proportion of the eligible participants in both arms provided informed consent. There was significant drop-off between consent and completion of the baseline survey in the PH arm, resulting in enrollment rates of 32.7% (35/107) for the PH arm and 67.9% (114/168) for the WEB arm (P<.001). The overall enrollment rate among everyone who clicked on a study ad was 2%. There were no between group differences in the proportion that installed the Facebook app (66/114, 57.9% WEB vs 17/35, 49% PH) or that completed the 30-day follow-up survey (49/114, 43.0% WEB vs 16/35, 46% PH). A total of $6074 was spent on ads, generating 3,834,289 impressions and resulting in 8446 clicks (average cost $0.72 per click). Per participant enrollment costs for advertising alone were $27 WEB and $87 PH.

Conclusions

A more intensive phone baseline assessment protocol yielded a lower rate of enrollment, equivalent follow-up rates, and higher enrollment costs compared to a Web-based assessment protocol. Future research should focus on honing mixed-mode assessment protocols to further optimize enrollment and retention.

Prevalence and Frequency of mHealth and eHealth Use Among US and UK Smokers and Differences by Motivation to Quit

BACKGROUND

Both mHealth and eHealth interventions for smoking cessation are rapidly being developed and tested. There are no data on use of mHealth and eHealth technologies by smokers in general or by smokers who are not motivated to quit smoking.

OBJECTIVE

The aims of our study were to (1) assess technology use (eg, texting, social media, Internet) among smokers in the United States and United Kingdom, (2) examine whether technology use differs between smokers who are motivated to quit and smokers who are not motivated to quit, (3) examine previous use of technology to assist with smoking cessation, and (4) examine future intentions to use technology to assist with smoking cessation.

METHODS

Participants were 1000 adult smokers (54.90%, 549/1000 female; mean age 43.9, SD 15.5 years; US: n=500, UK: n=500) who were recruited via online representative sampling strategies. Data were collected online and included demographics, smoking history, and frequency and patterns of technology use.

RESULTS

Among smokers in general, there was a high prevalence of mobile and smartphone ownership, sending and receiving texts, downloading and using apps, using Facebook, and visiting health-related websites. Smokers who were unmotivated to quit were significantly less likely to own a smartphone or handheld device that connects to the Internet than smokers motivated to quit. There was a significantly lower prevalence of sending text messages among US smokers unmotivated to quit (78.2%, 179/229) versus smokers motivated to quit (95.0%, 229/241), but no significant differences between the UK groups (motivated: 96.4%, 239/248; unmotivated: 94.9%, 223/235). Smokers unmotivated to quit in both countries were significantly less likely to use a handheld device to read email, play games, browse the Web, or visit health-related websites versus smokers motivated to quit. US smokers had a high prevalence of app downloads regardless of motivation to quit, but UK smokers who were motivated to quit had greater prevalence of app downloads than smokers unmotivated to quit. US smokers were significantly more likely to have a Facebook account (87.0%, 435/500) than UK smokers (76.4%, 382/500), but smokers unmotivated to quit in both countries used Facebook less frequently than smokers motivated to quit. Smokers who were unmotivated to quit were less likely to have used eHealth or mHealth platforms to help them quit smoking in the past and less likely to say that they would use them for smoking cessation in the future.

CONCLUSIONS

Although smokers unmotivated to quit make less use of technology than smokers motivated to quit, there is sufficient prevalence to make it worthwhile to develop eHealth and mHealth interventions to encourage cessation. Short and low-effort communications, such as text messaging, might be better for smokers who are less motivated to quit. Multiple channels may be required to reach unmotivated smokers.

A Scalable Framework to Detect Personal Health Mentions on Twitter

Background

Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual’s health.

Objective

The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.

Methods

We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.

Results

Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people’s health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author’s status, while only 1% (1/100) disclosed another person’s status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author’s status, while 21% (21/100) disclosed another person’s status.

Conclusions

It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

Internet-based implementation of non-pharmacological interventions of the "people getting a grip on arthritis" educational program: an international online knowledge translation randomized controlled trial design protocol

BACKGROUND

Rheumatoid arthritis (RA) affects 2.1% of the Australian population (1.5% males; 2.6% females), with the highest prevalence from ages 55 to over 75 years (4.4-6.1%). In Canada, RA affects approximately 0.9% of adults, and within 30 years that is expected to increase to 1.3%. With an aging population and a greater number of individuals with modifiable risk factors for chronic diseases, such as arthritis, there is an urgent need for co-care management of arthritic conditions. The increasing trend and present shifts in the health services and policy sectors suggest that digital information delivery is becoming more prominent. Therefore, it is necessary to further investigate the use of online resources for RA information delivery.

OBJECTIVE

The objective is to examine the effect of implementing an online program provided to patients with RA, the People Getting a Grip on Arthritis for RA (PGrip-RA) program, using information communication technologies (ie, Facebook and emails) in combination with arthritis health care professional support and electronic educational pamphlets. We believe this can serve as a useful and economical method of knowledge translation (KT).

METHODS

This KT randomized controlled trial will use a prospective randomized open-label blinded-endpoint design to compare four different intervention approaches of the PGrip-RA program to a control group receiving general electronic educational pamphlets self-management in RA via email. Depending on group allocation, links to the Arthritis Society PGrip-RA material will be provided either through Facebook or by email. One group will receive feedback online from trained health care professionals. The intervention period is 6 weeks. Participants will have access to the Internet-based material after the completion of the baseline questionnaires until the final follow-up questionnaire at 6 months. We will invite 396 patients from Canadian and Australian Arthritis Consumers' Associations to participate using online recruitment.

RESULTS

This study will build on a pilot study using Facebook, which revealed promising effects of knowledge acquisition/integration of the evidence-based self-management PGrip educational program.

CONCLUSIONS

The use of online techniques to disseminate knowledge provides an opportunity to reduce health care costs by facilitating self-management of people with arthritis. Study design strengths include the incorporation of randomization and allocation concealment to ensure internal validity. To avoid intergroup contamination, the Facebook group page security settings will be set to "closed", thus allowing only invited participants to access it. Study limitations include the lack of participant blinding due to the characteristics of this KT randomized controlled trial and a potential bias of recruiting patients only online, though this was proven effective in the previous pilot study.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12614000397617; http://www.anzctr.org.au/TrialSearch.aspx (Archived by WebCite at http://www.webcitation.org/6PrP0kQf8).

Facebook apps for smoking cessation: a review of content and adherence to evidence-based guidelines

BACKGROUND

Facebook is the most popular social network site, with over 1 billion users globally. There are millions of apps available within Facebook, many of which address health and health behavior change. Facebook may represent a promising channel to reach smokers with cessation interventions via apps. To date, there have been no published reports about Facebook apps for smoking cessation.

OBJECTIVE

The purpose of this study was to review the features and functionality of Facebook apps for smoking cessation and to determine the extent to which they adhere to evidence-based guidelines for tobacco dependence treatment.

METHODS

In August 2013, we searched Facebook and three top Internet search engines using smoking cessation keywords to identify relevant Facebook apps. Resultant apps were screened for eligibility (smoking cessation-related, English language, and functioning). Eligible apps were reviewed by 2 independent coders using a standardized coding scheme. Coding included content features (interactive, informational, and social) and adherence to an established 20-item index (possible score 0-40) derived from the US Public Health Service's Clinical Practice Guidelines for Treating Tobacco Use and Dependence.

RESULTS

We screened 22 apps for eligibility; of these, 12 underwent full coding. Only 9 apps were available on Facebook. Facebook apps fell into three broad categories: public pledge to quit (n=3), quit-date-based calculator/tracker (n=4), or a multicomponent quit smoking program (n=2). All apps incorporated interactive, informational, and social features except for two quit-date-based calculator/trackers apps (lacked informational component). All apps allowed app-related posting within Facebook (ie, on self/other Facebook profile), and four had a within-app "community" feature to enable app users to communicate with each other. Adherence index summary scores among Facebook apps were low overall (mean 15.1, SD 7.8, range 7-30), with multicomponent apps scoring the highest.

CONCLUSIONS

There are few smoking cessation apps available within Facebook. Among those available, adherence to cessation treatment guidelines was low. Smoking cessation interventions provided via the Facebook platform are a unique and as yet untapped treatment strategy that can harness existing social support and social networks for quitting. Research is needed to examine whether apps that adhere to clinical practice guidelines for tobacco dependence treatment are more effective in promoting cessation than those that do not.