Dataset Information

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking.

ABSTRACT: Current treatments for smoking yield suboptimal outcomes, partly because of an inability to reduce cue-induced smoking. Mindfulness training (MT) has shown preliminary efficacy for smoking cessation, yet its neurobiological target remains unknown. Our prior work with nonsmokers indicates that MT reduces posterior cingulate cortex (PCC) activity. In individuals who smoke, the PCC, consistently a main hub of the "default mode network," activates in response to smoking cues. In this randomized controlled trial, we tested the effects of app-delivered MT on PCC reactivity to smoking cues and whether individual differences in MT-mediated PCC changes predicted smoking outcomes. Smoking cue-induced PCC reactivity was measured using functional magnetic resonance imaging at baseline and 1 month after receiving smartphone app-based MT (n?=?33) vs. an active control (National Cancer Institute's QuitGuide, n?=?34). Whether individual differences in treatment-related changes in PCC activity predicted smoking behavior was assessed. The MT group demonstrated a significant correlation between a reduction in PCC reactivity to smoking cues and a decline in cigarette consumption (r?=?0.39, p?=?0.02). No association was found in the control group (r?=?0.08, p?=?0.65). No effects of group alone were found in PCC or cigarette reduction. Post hoc analysis revealed this association is sex specific (women, r?=?0.49, p?=?0.03; men: r?=?-0.08, p?=?0.79). This initial report indicates that MT specifically reduces smoking cue-induced PCC activity in a subject-specific manner, and the reduction in PCC activity predicts a concurrent decline in smoking. These findings link the hypothesized behavioral effects of MT for smoking to neural mechanisms particularly in women. This lays the groundwork for identifying individuals who may benefit from targeted digital therapeutic treatments such as smartphone-based MT, yielding improved clinical outcomes.

SUBMITTER: Janes AC

PROVIDER: S-EPMC6785102 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking.

Janes Amy C AC Datko Michael M Roy Alexandra A Barton Bruce B Druker Susan S Neal Carolyn C Ohashi Kyoko K Benoit Hanif H van Lutterveld Remko R Brewer Judson A JA

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20190430 9

Current treatments for smoking yield suboptimal outcomes, partly because of an inability to reduce cue-induced smoking. Mindfulness training (MT) has shown preliminary efficacy for smoking cessation, yet its neurobiological target remains unknown. Our prior work with nonsmokers indicates that MT reduces posterior cingulate cortex (PCC) activity. In individuals who smoke, the PCC, consistently a main hub of the "default mode network," activates in response to smoking cues. In this randomized cont ...[more]

PMID: 31039580

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Project description:Introduction:Cue reactivity (CR) research has reliably demonstrated robust cue-induced responding among smokers exposed to common proximal smoking cues (eg, cigarettes, lighter). More recent work demonstrates that distal stimuli, most notably the actual environments in which smoking previously occurred, can also gain associative control over craving. In the real world, proximal cues always occur within an environment; thus, a more informative test of how cues affect smokers might be to present these two cue types simultaneously. Methods:Using a combined-cue counterbalanced CR paradigm, the present study tested the impact of proximal (smoking and neutral) + personal environment (smoking and nonsmoking places) pictorial cues, on smokers' subjective and behavioral CR; as well as the extent to which cue-induced craving predicts immediate subsequent smoking in a within-subjects design. Results:As anticipated, the dual smoking cue combination (ProxS + EnvS) led to the greatest cue-induced craving relative to the other three cue combinations (ProxS + EnvN, ProxN + EnvS, and ProxN ± EnvN), ps < .004. Dual smoking cues also led to significantly shorter post-trial latencies to smoke, ps < .01. Overall CR difference score (post-trial craving minus baseline craving) was predictive of subsequent immediate smoking indexed by: post-trial latency to smoke [B = -2.69, SE = 9.02; t(143) = -2.98, p = .003]; total puff volume [B = 2.99, SE = 1.13; t(143) = 2.65, p = .009]; and total number of puffs [B = .053, SE = .027; t(143) = 1.95, p = .05]. Conclusions:The implications of these findings for better understanding the impact of cues on smoking behavior and cessation are discussed. Implications:This novel cue reactivity study examined smokers' reactivity to combined proximal and distal smoking cues. Exposure to a combination of two smoking cues (proximal and environment) led to the greatest increases in cue-induced craving and smoking behavior compared to all other cue combinations. Further, the overall magnitude of cue-induced craving was found to significantly predict immediate subsequent smoking. This work provides new insight on how exposure to various cues and cue combinations directly affect smokers' craving and actual smoking behavior, as well as the relationship between those two indices of reactivity.

Project description:IntroductionMindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling (ES) was used to measure real-time craving, smoking, and mindfulness.MethodsA researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) versus experience sampling only (ES) to (1) increase 1-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n = 143; ES n = 182; 72% female, 81% white, age 41 ± 12 year).ResultsNo group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1) = 0.43, p = .51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p < .0001), craving strength (p < .0001) and frequency (p < .0001), and an increase in mindfulness (p < .05). Using ES data, a craving by group interaction was observed (F(1,3785) = 3.71, p = .05) driven by a stronger positive association between craving and cigarettes per day for ES (t = 4.96, p < .0001) versus MMT-ES (t = 2.03, p = .04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104) = 4.44, p = .04).ConclusionsAlthough mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer term.ImplicationsThis is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based MMT-ES may lessen the association between craving and smoking.Trial registrationClinicaltrials.gov NCT02134509.

Project description:Mobile health technologies are being developed for personal lifestyle and medical healthcare support, of which a growing number are designed to assist smokers to quit. The potential impact of these technologies in the fight against smoking addiction and on improving quitting rates must be systematically evaluated. The aim of this report is to identify and appraise the most promising smoking detection and quitting technologies (e.g., smartphone apps, wearable devices) supporting smoking reduction or quitting programs. We searched PubMed and Scopus databases (2008-2019) for studies on mobile health technologies developed to assist smokers to quit using a combination of Medical Subject Headings topics and free text terms. A Google search was also performed to retrieve the most relevant smartphone apps for quitting smoking, considering the average user's rating and the ranking computed by the search engine algorithms. All included studies were evaluated using consolidated criteria for reporting qualitative research, such as applied methodologies and the performed evaluation protocol. Main outcome measures were usability and effectiveness of smoking detection and quitting technologies supporting smoking reduction or quitting programs. Our search identified 32 smoking detection and quitting technologies (12 smoking detection systems and 20 smoking quitting smartphone apps). Most of the existing apps for quitting smoking require the users to register every smoking event. Moreover, only a restricted group of them have been scientifically evaluated. The works supported by documented experimental evaluation show very high detection scores, however the experimental protocols usually lack in variability (e.g., only right-hand patients, not natural sequence of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios. Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.

Project description:ObjectivesThe use of mobile technology for smoking cessation holds promise for adolescents, who do not typically access traditional treatments, but most are not grounded in theory or mechanism. Operant conditioning theory suggests an addictive smoking loop is formed between nicotine use and affective states, leading to habitual cue-induced craving and automatic behavior; mindfulness training may bring automated smoking behavior into awareness, so smokers may work mindfully with cravings. Mindfulness training delivered via smartphone technology therefore has potential to help adolescent smokers break this addictive loop and quit smoking. This pair-matched cluster-randomized controlled school-based pilot study evaluated program feasibility and preliminary smoking outcomes in relation to intervention engagement.MethodsSix high schools were pair matched and randomly assigned to one of three interventions: (1) mindfulness training delivered via mobile smoking cessation application (Craving to Quit, C2Q), (2) NCI's QuitSTART smoking cessation application (NCI), and (3) written cessation materials (Materials). Adolescents (n = 146) smoking 5 or more cigarettes per day were recruited. Interventions were implemented over four weeks and study assessments were collected at baseline and 3- and 6- month follow-up, including self-reported 7-day point prevalence abstinence, program usage, smoking-related measures, and psychosocial factors.ResultsOverall cotinine-validated abstinence at 6 months was 15.8% and was similar between conditions. Odds of abstinence increased with each quartile increase in app/materials use with no significant differences between conditions (OR=1.60 (C2Q), 1.66 (Materials), and 2.69 (NCI)). Of participants still smoking at 6 months, for each quartile increase in engagement the number of cigarettes smoked in the previous 7 days showed a significantly greater decline in the C2Q condition (-5.71) compared to the Materials (-0.95) and NCI (+7.73) condition (p=0.02 for differences between conditions).ConclusionsCotinine-validated abstinence was similar between intervention conditions and tended to increase with greater engagement in each condition. Greater C2Q app engagement among continuing smokers was associated with a significantly greater decline in number of cigarettes smoked compared to the other conditions. The Craving to Quit (C2Q) mobile smoking cessation application with mindfulness training was feasible to use and has promise in assisting adolescents to quit or decrease cigarette smoking.Clinical trial registrationDeveloping a Smartphone App with Mindfulness Training for Teen Smoking Cessation: ClinicalTrials.gov Identifier: NCT02218281.

Project description:BackgroundCigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status.MethodWe developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes.ResultsUsing the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88-0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13-1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25-1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07-1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes.ConclusionsHIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients.

Dataset Information

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking.

Publications

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking.

Similar Datasets

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