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.

Background and aims Contingency management (CM) is one of the most effective behavioral interventions to promote drug abstinence, but availability of this treatment is limited. We evaluated the efficacy and acceptability of Internet-based CM relative to an Internet-based monitoring and goal setting control group in a nationwide sample of cigarette smokers. Design Randomized controlled trial with 3- and 6-month follow ups. Setting USA. Participants Smokers (n=94) from 26 states were enrolled (mean age 36, 56% female). Intervention and comparator Participants were randomized to earn financial incentives (up to $480 over 7 weeks) based on video-verified abstinence using breath carbon monoxide (CO) output (n=48; Abstinent Contingent Group, AC), or based on submitting CO samples (n=46, Submission Contingent, SC). Both groups also received the same CO-based goals. A $50 deposit was required in both groups that could be recouped from initial earnings. Measures The primary outcome was point prevalence at week 4. Secondary outcomes were point prevalence at the 3- and 6-month follow-ups, percentages of negative CO samples, adherence to the CO sampling protocol, and treatment acceptability ratings on a 0–100mm visual analog scale. Findings Abstinence rates differed at 4 weeks between the AC (39.6%) and SC (13.0%) groups (odds ratio=4.4, 95% CI=1.6–12.3), but not at the 3- (29.2% AC and 19.6% SC, odds ratio=1.7, 95% CI=.6–4.4), or 6- (22.9% AC and 13.0% SC, odds ratio=2.0, 95% CI=.7–5.9) month follow-ups. During the two main treatment phases, there were significant differences in negative COs (53.9% AC and 24.8% SC, odds ratio = 3.5, 95% CI=3.1–4.0; 43.4% AC and 24.6% SC, odds ratio = 2.3, 95% CI=1.6–3.4). Adherence to the CO submission protocol was equivalent (78% AC and 85% SC, difference = 7.0%, 95% CI = −10.3%–23.8%, x2=.75, p = .39). The lowest acceptability ratings were for the items assessing the deposit, whereas the highest ratings concerned the ease of the intervention, the graph of CO results, and earning money. Conclusions A contingency management/financial incentive program delivered via the Internet improved short-term abstinence rates compared with an internet program without the incentives.

No abstract

Although IPS was shown to be an effective model for helping justice-involved clients with severe mental illness achieve employment, the outcomes were modest compared with those in prior IPS studies. The IPS model provided a useful framework for employment services for this population, but augmentations may be needed.

BackgroundSocial media technologies offer a novel opportunity for scalable health interventions that can facilitate user engagement and social support, which in turn may reinforce positive processes for behavior change.ObjectiveBy using principles from health communication and social support literature, we implemented a Facebook group–based intervention that targeted smoking reduction and cessation. This study hypothesized that participants’ engagement with and perceived social support from our Facebook group intervention would predict smoking reduction.MethodsWe recruited 16 regular smokers who live in the United States and who were motivated in quitting smoking at screening. We promoted message exposure as well as engagement and social support systems throughout the intervention. For message exposure, we posted prevalidated, antismoking messages (such as national antismoking campaigns) on our smoking reduction and cessation Facebook group. For engagement and social support systems, we delivered a high degree of engagement and social support systems during the second and third week of the intervention and a low degree of engagement and social support systems during the first and fourth week. A total of six surveys were conducted via Amazon Mechanical Turk (MTurk) at baseline on a weekly basis and at a 2-week follow-up.ResultsOf the total 16 participants, most were female (n=13, 81%), white (n=15, 94%), and between 25 and 50 years of age (mean 34.75, SD 8.15). There was no study attrition throughout the 6-time-point baseline, weekly, and follow-up surveys. We generated Facebook engagement and social support composite scores (mean 19.19, SD 24.35) by combining the number of likes each participant received and the number of comments or wall posts each participant posted on our smoking reduction and cessation Facebook group during the intervention period. The primary outcome was smoking reduction in the past 7 days measured at baseline and at the two-week follow-up. Compared with the baseline, participants reported smoking an average of 60.56 fewer cigarettes per week (SD 38.83) at the follow-up, and 4 participants out of 16 (25%) reported 7-day point prevalence smoking abstinence at the follow-up. Adjusted linear regression models revealed that a one-unit increase in the Facebook engagement and social support composite scores predicted a 0.56-unit decrease in cigarettes smoked per week (standard error =.24, P=.04, 95% CI 0.024-1.09) when baseline readiness to quit, gender, and baseline smoking status were controlled (F4, 11=8.85, P=.002).ConclusionsThis study is the first Facebook group–based intervention that systemically implemented health communication strategies and engagement and social support systems to promote smoking reduction and cessation. Our findings imply that receiving one like or posting on the Facebook-based intervention platform predicted smoking approximately one less cigarette in the past 7 days, and that interventions should facilitate user interactions to foster user engagement and social supp...

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

BackgroundExcess screen media use is a robust predictor of childhood obesity. Understanding how household factors may affect children’s screen use is needed to tailor effective intervention efforts. The preschool years are a critical time for obesity prevention, and while it is likely that greater household disorder influences preschool-aged children’s screen use, data on that relationship are absent. In this study, our goal was to quantify the relationships between household chaos and screen use in preschool-aged children.MethodsA cross-sectional, online survey was administered to 385 parents of 2–5 year-olds recruited in 2017. Household chaos was measured with the Confusion, Hubbub and Order Scale (i.e., the chaos scale), a validated, parent-reported scale. The scale consists of 15 items, each scored on a 4-point Likert scale. Final scores were the sum across the 15 items and modeled as quartiles for analyses. Parents reported their children’s screen use for nine electronic media activities. Adjusted linear and Poisson regression were used to model associations between household chaos and children’s total weekly screen use, screen use within one hour of bedtime and screen use in the bedroom.ResultsChildren averaged 31.0 (SD = 23.8) hours per week with screens, 49.6% used screens within one hour of bedtime and 41.0% used screens in their bedrooms. In adjusted regression models, greater household chaos was positively associated with weekly screen use (P = 0.03) and use of screens within one hour of bedtime (P < 0.01) in a dose-dependent manner. Children in the fourth versus the first quartile of household chaos were more likely to use screens in their bedroom (P = 0.03).ConclusionsGreater household chaos was associated with increased total screen use as well as screen use behaviors that are related to disrupted nighttime sleep. Findings suggest that household chaos may be an obesity risk factor during the preschool years because of such effects on screen use, and highlight the need to consider household chaos when implementing home-based obesity prevention programs for young children.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-6113-2) contains supplementary material, which is available to authorized users.

Purpose This study examined the concordance between individuals’ self‐reported rural‐urban category of their community and ZIP Code‐derived Rural‐Urban Commuting Area (RUCA) category. Methods An Internet‐based survey, administered from August 2017 through November 2017, was used to collect participants’ sociodemographic characteristics, self‐reported ZIP Code of residence, and perception of which RUCA category best describes the community in which they live. We calculated weighted kappa (ĸ) coefficients (95% confidence interval [CI]) to test for concordance between participants’ ZIP Code‐derived RUCA category and their selection of RUCA descriptor. Descriptive frequency distributions of participants' demographics are presented. Findings A total of 622 survey participants, residents of New Hampshire (63%) and Vermont (37%), responded to the survey's self‐reported rural‐urban category. The overall ĸ was 0.33 (95% CI: 0.27‐0.38). The highest concordance was found among those living in a small rural area (N = 81, 13%): 62% of this group identified their communities as small rural. Sixty‐five percent (300/459) of participants residing in urban or large rural areas reported their community as more rural (small rural or isolated). Sixty‐eight percent (111/163) of participants living in small rural or isolated areas identified their community as more urban (large rural or urban). Conclusions Discordance was found between self‐report of rural‐urban category and ZIP Code‐derived RUCA designation. Caution is warranted when attributing rural‐urban designation to individuals based on geographic unit, since perceived rurality/urbanicity of their community that relates to health behaviors may not be reflected.