Longitudinal analysis of peer social support and quitting Smoking: Moderation by sex and implications for cessation interventions.

Social support predicts better health and lower mortality, but the benefits of peer social support for helping cigarette smokers quit are unclear. Moreover, sex as a moderating factor has not been investigated despite sex differences in social support processes. This study of smokers' perceived availability of peer social support in quitting cigarette smoking is a secondary analysis of 1,010 individuals enrolled in an RCT that provided quitting assistance using tailored emails scheduled around a quit date. Participants completed measures of peer support for quitting cigarettes at enrollment (baseline), and at 1-, 3-, and 6-month follow-ups. Peer support at follow-ups was categorized as never-present, always-present, or mixed. A Generalized Estimating Equation (GEE) examined the association between peer support and 7-day point prevalence abstinence at follow-ups, controlling for baseline peer support, experimental condition, stress, depression, and sociodemographic and smoking behavior-related variables. Interactions of peer support × time, and peer support × sex, were tested. Results indicated that among women, always-present or mixed peer support was associated with, respectively, odds of abstinence that were 4.36 (95 % CI, 2.54-7.49, p = 0.0001), and 2.21 (OR = 2.21, 95 % CI, 1.27-3.85, p = 0.005) greater than among women reporting never-present peer support. Among men, peer support did not predict abstinence. Women who smoke may be especially receptive to the benefits of peer support when attempting to quit. Investigation of the basis of their perceptions, how they might be increased, and whether interventions to change them would be effective, is warranted.

Oncology team perspectives on distress screening: a multisite study of a well-established use of patient-reported outcomes for clinical assessment.

PURPOSE: Cancer care team attitudes towards distress screening are key to its success and sustainability. Previous qualitative research has interviewed staff mostly around the startup phase. We evaluate oncology teams' perspectives on psychosocial distress screening, including perceived strengths and challenges, in settings where it has been operational for years. METHODS: We conducted, transcribed, and analyzed semi-structured interviews with 71 cancer care team members (e.g., MDs, RNs, MSWs) at 18 Commission on Cancer-accredited cancer programs including those serving underrepresented populations. RESULTS: Strengths of distress screening identified by participants included identifying patient needs and testing provider assumptions. Staff indicated it improved patient-provider communication and other aspects of care. Challenges to distress screening included patient barriers (e.g., respondent burden) and lack of electronic system interoperability. Participants expressed the strengths of distress screening (n = 291) more than challenges (n = 86). Suggested improvements included use of technology to collect data, report results, and make referrals; complete screenings prior to appointments; longitudinal assessment; additional staff training; and improve resources to address patient needs. CONCLUSION: Cancer care team members' perspectives on well-established distress screening programs largely replicate findings of previous studies focusing on the startup phase, but there are important differences: team members expressed more strengths than challenges, suggesting a positive attitude. While our sample described many challenges described previously, they did not indicate challenges with scoring and interpreting the distress screening questionnaire. The differences in attitudes expressed in response to mature versus startup implementations provide important insights to inform efforts to sustain and optimize distress screening.

Peer Mentoring and Automated Text Messages for Smoking Cessation: A Randomized Pilot Trial.

INTRODUCTION: Text-messaging programs for smoking cessation, while efficacious, have high dropout rates. To address this problem, we developed and tested the feasibility and early efficacy of a peer-mentoring intervention for smoking cessation provided by former smokers. METHODS: Adult US smokers were recruited nationally into a randomized pilot trial (N = 200), comparing 6-8 weeks of automated text-messaging support (SmokefreeTXT) and automated text support plus personalized texts from a peer mentor who formerly smoked. The primary outcome was biochemically verified 7-day point-prevalence abstinence at 3 months post-quit date, assessed on an intention-to-treat basis (missing = smoking). Self-reported abstinence, program acceptability, user engagement, and user perceptions were also assessed. RESULTS: Biochemically verified abstinence at 3 months was 7.9% (8/101) in the intervention group and 3.0% (3/99) in the control group (adjusted difference 6.5, 95% CI = 0.7% to 12.3%; p = .03). Self-reported abstinence at 3 months was 23.8% (24/101) in the intervention group versus 13.1% (13/99) in the control group (adjusted difference 12.7, 95% CI = 1.2% to 24.1%; p = .03). The intervention had a positive but insignificant effect on overall satisfaction (78.3% vs. 72.9% control group, p = .55). Having a mentor did not significantly alter duration of interaction with the program nor the proportion unsubscribing, although the intervention group reset their quit date with greater frequency (p < .01) and sent more messages (p < .01). CONCLUSIONS: Peer mentoring combined with automated text messages was feasible and acceptable and increased smoking abstinence compared with automated messages alone. The results highlight the promise of this intervention approach and the need for a full-scale evaluation. IMPLICATIONS: Providing quitting assistance by automated text messaging has been shown to increase smoking abstinence. Yet, dropout rates in text-messaging programs are high. No studies have tested the effectiveness of peer mentors who are former smokers as part of a text-messaging intervention, although they represent a promising way to retain, engage, and support smokers. This randomized pilot trial suggests that peer mentors can complement automated text-messaging programs to promote smoking abstinence.

Trust of Information about Tobacco and E-Cigarettes from Health Professionals versus Tobacco or Electronic Cigarette Companies: Differences by Subgroups and Implications for Tobacco Messaging.

Smoking behavior may be influenced by perceived trust of information from tobacco and e-cigarette companies about their products. The purpose of this study was to identify sociodemographic subgroups with more trust in tobacco product (tobacco and e-cigarette) companies than health professionals and explore associations between this relative trust and tobacco product use.Health Information National Trends Survey (HINTS 4, FDA Cycle) data were analyzed (N = 3,738). Two relative trust measures were developed identifying respondents who trust tobacco companies or e-cigarette companies as much as or more than health professionals for information about the health effects of tobacco products or e-cigarettes versus those who place more trust in health professionals. Dependent variables were smoking status (current, former, never) and e-cigarette use (ever, never). Bivariate analyses and multivariable logistic regressions were conducted in SAS 9.4 using jackknife replicate weights.Respondents who trusted tobacco or e-cigarette companies as much as or more than health professionals were disproportionately from racial/ethnic minority groups or had low levels of income or education (all p < 0.05). Relative trust was not associated with smoking status. After controlling for demographics, respondents who trusted e-cigarette companies as much as or more than health professionals had 87% greater odds (95% CI: 1.16, 3.00) of e-cigarette use, compared to respondents who placed higher trust in health professionals.Findings suggest that population subgroups with greater trust in e-cigarette companies relative to health professionals are more prone to e-cigarette use. Targeted communication strategies may be needed for underserved populations and to counter messaging from e-cigarette companies.

To Text or Not to Text? Technology-based Cessation Communication Preferences among Urban, Socioeconomically Disadvantaged Smokers.

Objective: Effective smoking cessation interventions are needed to reduce tobacco-related disparities. Communication technology-based interventions are increasingly being employed to help smokers quit, with controlled research demonstrating efficacy of text messaging and email in increasing abstinence. Understanding preferences for such strategies among socioeconomically disadvantaged smokers can inform targeted intervention planning. The aims of this study were to: 1) examine socioeconomically disadvantaged smokers' use of and access to communication technology; and 2) elucidate preferences for receiving quitting information and support via email and text message. Design: This cross-sectional, mixed-methods study collected data from a self-administered survey and focus groups in September 2017. Participants: A community-based, sample of 15 predominantly African American, socioeconomically disadvantaged smokers aged 21-64 years. Results: Smartphone ownership was high, although use of communication-based cessation resources such as web sites and smartphone apps was low. Four themes emerged relevant to preferences for receiving quitting information and support via email and text message: access, appropriateness, intended use, and satisfaction. Although initially participants were mixed in their preferences for receiving emails vs texts, 80% preferred emails over texts when presented with sample emails and text messages containing cessation information. Conclusions: In this sample of socioeconomically disadvantaged smokers, emails were preferred over text messages for smoking cessation assistance. Although both email and text message strategies may be acceptable to socioeconomically disadvantaged smokers generally, issues such as access and intended use should be considered to inform specific disparity-reducing intervention approaches.

Randomised controlled trial of stand-alone tailored emails for smoking cessation.

INTRODUCTION: Digital technology has created opportunities for delivering smoking cessation assistance at the population level. However, the efficacy of sending multiple, automated, tailored emails providing motivation, support and information for quitting is unknown. METHODS: Smokers planning to quit (n=1070) were randomly assigned to (1) 27 tailored cessation emails (deluxe email group (DEG)), (2) 3 to 4 tailored emails with links to downloadable booklets (basic email group (BEG)) or (3) a single non-tailored email (single email group (SEG)). All emails included links to quitting resources. Self-reported 7-day point-prevalence abstinence was assessed at 1 month, 3 months and 6 months postenrolment. RESULTS: Across follow-ups, abstinence was significantly greater for smokers in the DEG (34%) compared with the SEG (25.8%; OR=1.47, 95% CI 1.07 to 2.02, p=0.02) but there was no difference between the BEG (30.8%) and the SEG (p=0.13). Results were independent of baseline cigarettes per day, interest in quitting, smoker in household, use of nicotine replacement therapy (NRT) or varenicline and gender, themselves associated with abstinence (ps<0.05). Missing=smoking and multiple imputation analyses based on 25 data sets corroborated results. Participants in the DEG were also more likely to use non-medication aids (eg, quit smoking website, cessation class/clinic) compared with the SEG (OR=1.34, p=0.02, CI 1.06 to 1.71), but use of these or NRT by the 4-week follow-up (vs no use) increased abstinence across follow-ups primarily for those in the SEG. CONCLUSIONS: Stand-alone tailored, multiple emails providing support, motivation and information during a quit attempt are an easily deployable, inexpensive mode of providing effective cessation assistance to large numbers of smokers planning to quit.

A content analysis of popular smartphone apps for smoking cessation.

BACKGROUND: Smartphone applications (apps) are increasingly available for smoking cessation. PURPOSE: This study examined the content of popular apps for smoking cessation for both iPhone and Android operating systems in February 2012. METHODS: A total of 252 smoking-cessation apps were identified for the iPhone and 148 for the Android. Across both operating systems, the most popular apps were identified (n=47 for the iPhone and n=51 for the Android) and analyzed for their (1) approach to smoking cessation and (2) adherence to an index based on the U.S. Public Health Service's Clinical Practice Guidelines for Treating Tobacco Use and Dependence. Where available, apps were coded for frequency of downloads. The analysis took place in 2012. RESULTS: Overall, popular apps have low levels of adherence, with an average score of 12.9 of a possible 42 on the Adherence Index. No apps recommended calling a quitline, and only a handful of apps recommended using approved medications (4.1%). Android apps in the sample were downloaded worldwide between 310,800 and 1,248,000 times per month. For both the iPhone and Android, user ratings were positively associated with scores on the Adherence Index. For the iPhone, display order was also positively associated with scores on the Adherence Index. CONCLUSIONS: Apps could be improved by better integration with the Clinical Practice Guidelines and other evidence-based practices.

Using the internet to understand smokers' treatment preferences: informing strategies to increase demand.

BACKGROUND: Most smokers attempt to quit on their own even though cessation aids can substantially increase their chances of success. Millions of smokers seek cessation advice on the Internet, so using it to promote cessation products and services is one strategy for increasing demand for treatments. Little is known, however, about what cessation aids these smokers would find most appealing or what predicts their preferences (eg, age, level of dependence, or timing of quit date). OBJECTIVE: The objective of our study was to gain insight into how Internet seekers of cessation information make judgments about their preferences for treatments, and to identify sociodemographic and other predictors of preferences. METHODS: An online survey assessing interest in 9 evidence-based cessation products and services was voluntarily completed by 1196 smokers who visited the American Cancer Society's Great American Smokeout (GASO) webpage. Cluster analysis was conducted on ratings of interest. RESULTS: In total, 48% (572/1196) of respondents were "quite a bit" or "very much" interested in nicotine replacement therapy (NRT), 45% (534/1196) in a website that provides customized quitting advice, and 37% (447/1196) in prescription medications. Only 11.5% (138/1196) indicated similar interest in quitlines, and 17% (208/1196) in receiving customized text messages. Hierarchical agglomerative cluster analysis revealed that interest in treatments formed 3 clusters: interpersonal-supportive methods (eg, telephone counseling, Web-based peer support, and in-person group programs), nonsocial-informational methods (eg, Internet programs, tailored emails, and informational booklets), and pharmacotherapy (NRT, bupropion, and varenicline). Only 5% (60/1196) of smokers were "quite a bit" or "very much" interested in interpersonal-supportive methods compared with 25% (298/1196) for nonsocial-informational methods and 33% (399/1196) for pharmacotherapy. Multivariate analyses and follow-up comparisons indicated that level of interest in pharmacotherapy ("quite a bit or "very much" vs. "not at all") varied as a function of education (n = 575, χ(2) (3) =16.6, P = .001), age (n = 528, χ(2) (3) = 8.2, P = .04), smoking level (n = 514, χ(2) (3) = 9.5, P = .02), and when smokers were planning to quit (n = 607, χ(2) (4) = 34.0, P < .001). Surprisingly, greater age was associated with stronger interest in nonsocial-informational methods (n = 367, χ(2) (3) = 10.8, P = .01). Interest in interpersonal-supportive methods was greater if smokers had used a quitline before (n = 259, χ(2) (1) = 18.3, P < .001), or were planning to quit earlier rather than later (n = 148, χ(2) (1) = 4.9, P = .03). CONCLUSIONS: Smokers accessing the Internet for information on quitting appear to differentiate cessation treatments by how much interpersonal interaction or support the treatment entails. Quitting date, smoking level, and sociodemographic variables can identify smokers with varying levels of interest in the 3 classes of cessation methods identified. These results can potentially be used to more effectively target and increase demand for these treatments among smokers searching the Internet for cessation information.

Social support in smoking cessation: reconciling theory and evidence.

INTRODUCTION: The majority of smokers attempt to quit smoking on their own, but in any given year, only 5% or less are successful. To improve cessation rates, tapping social networks for social support during quitting has been recommended or tested in some interventions. Prior reviews of this research, however, have concluded that there is little to no evidence that partner support interventions are effective. DISCUSSION: Given the theoretical importance of the concept of social support, its demonstrated value in treatments that are implicitly supportive (e.g., telephone counseling), and the general lack of a guiding conceptual framework for research on the effects of peer or partner support for cessation, we describe theoretical models that explicitly incorporate social support constructs in predicting motivation for and success in quitting. CONCLUSION: Better differentiation of support concepts and elucidating causal pathways will lead to studies that demonstrate the value of social relationships in improving smokers' likelihood of cessation.