scholarly journals Comparison of mHealth and Face-to-Face Interventions for Smoking Cessation Among People Living With HIV: Meta-Analysis (Preprint)

2017 ◽  
Author(s):  
Olalekan A Uthman ◽  
Chidozie U Nduka ◽  
Mustapha Abba ◽  
Rocio Enriquez ◽  
Helena Nordenstedt ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Treatment Effects ◽  
People Living With Hiv ◽  
Short Term ◽  
Smoking Cessation Treatment ◽  
Face To Face ◽  
Cessation Treatment ◽  
Meta Analyses ◽  
Living With Hiv

BACKGROUND The prevalence of smoking among people living with HIV (PLHIV) is higher than that reported in the general population, and it is a significant risk factor for noncommunicable diseases in this group. Mobile phone interventions to promote healthier behaviors (mobile health, mHealth) have the potential to reach a large number of people at a low cost. It has been hypothesized that mHealth interventions may not be as effective as face-to-face strategies in achieving smoking cessation, but there is no systematic evidence to support this, especially among PLHIV. OBJECTIVE This study aimed to compare two modes of intervention delivery (mHealth vs face-to-face) for smoking cessation among PLHIV. METHODS Literature on randomized controlled trials (RCTs) investigating effects of mHealth or face-to-face intervention strategies on short-term (4 weeks to <6 months) and long-term (≥6 months) smoking abstinence among PLHIV was sought. We systematically reviewed relevant RCTs and conducted pairwise meta-analyses to estimate relative treatment effects of mHealth and face-to-face interventions using standard care as comparison. Given the absence of head-to-head trials comparing mHealth with face-to-face interventions, we performed adjusted indirect comparison meta-analyses to compare these interventions. RESULTS A total of 10 studies involving 1772 PLHIV met the inclusion criteria. The average age of the study population was 45 years, and women comprised about 37%. In the short term, mHealth-delivered interventions were significantly more efficacious in increasing smoking cessation than no intervention control (risk ratio, RR, 2.81, 95% CI 1.44-5.49; n=726) and face-to-face interventions (RR 2.31, 95% CI 1.13-4.72; n=726). In the short term, face-to-face interventions were no more effective than no intervention in increasing smoking cessation (RR 1.22, 95% CI 0.94-1.58; n=1144). In terms of achieving long-term results among PLHIV, there was no significant difference in the rates of smoking cessation between those who received mHealth-delivered interventions, face-to-face interventions, or no intervention. Trial sequential analysis showed that only 15.16% (726/1304) and 5.56% (632/11,364) of the required information sizes were accrued to accept or reject a 25% relative risk reduction for short- and long-term smoking cessation treatment effects. In addition, sequential monitoring boundaries were not crossed, indicating that the cumulative evidence may be unreliable and inconclusive. CONCLUSIONS Compared with face-to-face interventions, mHealth-delivered interventions can better increase smoking cessation rate in the short term. The evidence that mHealth increases smoking cessation rate in the short term is encouraging but not sufficient to allow a definitive conclusion presently. Future research should focus on strategies for sustaining smoking cessation treatment effects among PLHIV in the long term.

scholarly journals Delivery and implementation of an algorithm for smoking cessation treatment for people living with HIV and AIDS

AIDS Care ◽  
2019 ◽  
Vol 32 (2) ◽  
pp. 223-229 ◽  
Author(s):  
Karen L. Cropsey ◽  
Madelyne C. Bean ◽  
Louise Haynes ◽  
Matthew J. Carpenter ◽  
Lauren E. Richey
Keyword(s):  
Smoking Cessation ◽  
Hiv And Aids ◽  
People Living With Hiv ◽  
Smoking Cessation Treatment ◽  
Cessation Treatment ◽  
Living With Hiv

“Impact of Smoking Cessation Treatment” on Lung Function and Response Rate in EGFR Mutated Patients: A Short-Term Cohort Study

2015 ◽  
Vol 10 (3) ◽  
pp. 342-351 ◽  
Author(s):  
Aldo Pezzuto ◽  
Luciano Stumbo ◽  
Marco Russano ◽  
Pierfilippo Crucitti ◽  
Simone Scarlata ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Cohort Study ◽  
Lung Function ◽  
Response Rate ◽  
Short Term ◽  
Smoking Cessation Treatment ◽  
Cessation Treatment ◽  
Egfr Mutated

scholarly journals Adherence to Blended or Face-to-Face Smoking Cessation Treatment and Predictors of Adherence: Randomized Controlled Trial

10.2196/17207 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17207
Author(s):  
Lutz Siemer ◽  
Marjolein G J Brusse-Keizer ◽  
Marloes G Postel ◽  
Somaya Ben Allouch ◽  
Robbert Sanderman ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Treatment Time ◽  
Patient Characteristics ◽  
Smoking Cessation Treatment ◽  
Face To Face ◽  
Cessation Treatment ◽  
The Face ◽  
Blended Treatment ◽  
Predictors Of Adherence ◽  
The Web

Background Blended face-to-face and web-based treatment is a promising way to deliver smoking cessation treatment. Since adherence has been shown to be an indicator of treatment acceptability and a determinant for effectiveness, we explored and compared adherence and predictors of adherence to blended and face-to-face alone smoking cessation treatments with similar content and intensity. Objective The objectives of this study were (1) to compare adherence to a blended smoking cessation treatment with adherence to a face-to-face treatment; (2) to compare adherence within the blended treatment to its face-to-face mode and web mode; and (3) to determine baseline predictors of adherence to both treatments as well as (4) the predictors to both modes of the blended treatment. Methods We calculated the total duration of treatment exposure for patients (N=292) of a Dutch outpatient smoking cessation clinic who were randomly assigned either to the blended smoking cessation treatment (n=130) or to a face-to-face treatment with identical components (n=162). For both treatments (blended and face-to-face) and for the two modes of delivery within the blended treatment (face-to-face vs web mode), adherence levels (ie, treatment time) were compared and the predictors of adherence were identified within 33 demographic, smoking-related, and health-related patient characteristics. Results We found no significant difference in adherence between the blended and the face-to-face treatments. Participants in the blended treatment group spent an average of 246 minutes in treatment (median 106.7% of intended treatment time, IQR 150%-355%) and participants in the face-to-face group spent 238 minutes (median 103.3% of intended treatment time, IQR 150%-330%). Within the blended group, adherence to the face-to-face mode was twice as high as that to the web mode. Participants in the blended group spent an average of 198 minutes (SD 120) in face-to-face mode (152% of the intended treatment time) and 75 minutes (SD 53) in web mode (75% of the intended treatment time). Higher age was the only characteristic consistently found to uniquely predict higher adherence in both the blended and face-to-face groups. For the face-to-face group, more social support for smoking cessation was also predictive of higher adherence. The variability in adherence explained by these predictors was rather low (blended R2=0.049; face-to-face R2=0.076). Within the blended group, living without children predicted higher adherence to the face-to-face mode (R2=0.034), independent of age. Higher adherence to the web mode of the blended treatment was predicted by a combination of an extrinsic motivation to quit, a less negative attitude toward quitting, and less health complaints (R2=0.164). Conclusions This study represents one of the first attempts to thoroughly compare adherence and predictors of adherence of a blended smoking cessation treatment to an equivalent face-to-face treatment. Interestingly, although the overall adherence to both treatments appeared to be high, adherence within the blended treatment was much higher for the face-to-face mode than for the web mode. This supports the idea that in blended treatment, one mode of delivery can compensate for the weaknesses of the other. Higher age was found to be a common predictor of adherence to the treatments. The low variance in adherence predicted by the characteristics examined in this study suggests that other variables such as provider-related health system factors and time-varying patient characteristics should be explored in future research. Trial Registration Netherlands Trial Register NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113

scholarly journals Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence (Preprint)

2018 ◽  
Author(s):  
Lutz Siemer ◽  
Marjolein GJ Brusse-Keizer ◽  
Marloes G Postel ◽  
Somaya Ben Allouch ◽  
Angelos Patrinopoulos Bougioukas ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Marital Status ◽  
Adherence Rate ◽  
Social Modeling ◽  
Web Based ◽  
Smoking Cessation Treatment ◽  
Face To Face ◽  
Cessation Treatment ◽  
Number Of Patients ◽  
Predictors Of Adherence

BACKGROUND Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment’s acceptability and a determinant for treatment’s effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT). OBJECTIVE The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be used as a threshold for being adherent; (3) estimate adherence to BSCT; and (4) explore the possible predictors of adherence to BSCT. METHODS The data of patients (N=75) were analyzed to trace adherence to BSCT delivered at an outpatient smoking cessation clinic. In total, 18 patient activities (eg, using a Web-based smoking diary tool or responding to counselors’ messages) were selected to measure adherence; the degree of adherence per patient was compared with quitting success. The minimum degree of adherence of patients who reported abstinence was examined to define a threshold for the detection of adherent patients. The number of adherent patients was calculated for each of the 18 selected activities; the degree of adherence over the course of the treatment was displayed; and the number of patients who were adherent was analyzed. The relationship between adherence and 33 person-, smoking-, and health-related characteristics was examined. RESULTS The method for measuring adherence was found to be adequate as adherence to BSCT correlated with self-reported abstinence (P=.03). Patients reporting abstinence adhered to at least 61% of BSCT. Adherence declined over the course of the treatment; the percentage of adherent patients per treatment activity ranged from 82% at the start of the treatment to 11%-19% at the final-third of BSCT; applying a 61% threshold, 18% of the patients were classified as adherent. Marital status and social modeling were the best independent predictors of adherence. Patients having a partner had 11-times higher odds of being adherent (OR [odds ratio]=11.3; CI: 1.33-98.99; P=.03). For social modeling, graded from 0 (=partner and friends are not smoking) to 8 (=both partner and nearly all friends are smoking), each unit increase was associated with 28% lower odds of being adherent (OR=0.72; CI: 0.55-0.94; P=.02). CONCLUSIONS The current study is the first to explore adherence to a blended face-to-face and Web-based treatment (BSCT) based on a substantial group of patients. It revealed a rather low adherence rate to BSCT. The method for measuring adherence to BSCT could be considered adequate because the expected dose-response relationship between adherence and quitting could be verified. Furthermore, this study revealed that marital status and social modeling were independent predictors of adherence. CLINICALTRIAL Netherlands Trial Registry NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113 (Archived by WebCite at http://www.webcitation.org/71BAPwER8).

scholarly journals Validity of urges to smoke measures in predicting smoking relapse during treatment in primary care

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Daniel Kotz ◽  
Carolien van Rossem ◽  
Wolfgang Viechtbauer ◽  
Mark Spigt ◽  
Onno C. P. van Schayck
Keyword(s):  
Primary Care ◽  
Smoking Cessation ◽  
Diagnostic Value ◽  
Predictive Values ◽  
Smoking Cessation Treatment ◽  
Cut Point ◽  
Point Increase ◽  
Cessation Treatment ◽  
Measured Time

AbstractIn the context of smoking cessation treatment in primary care, identifying patients at the highest risk of relapse is relevant. We explored data from a primary care trial to assess the validity of two simple urges to smoke questions in predicting long-term relapse and their diagnostic value. Of 295 patients who received behavioural support and varenicline, 180 were abstinent at week 9. In this subgroup, we measured time spent with urges to smoke (TSU) and strength of urges to smoke (SUT; both scales 1 to 6 = highest). We used separate regression models with TSU or SUT as predictor and relapse from week 9–26 or week 9–52 as an outcome. We also calculated the sensitivity (SP), specificity and positive predictive values (PPV) of TSU and SUT in correctly identifying patients who relapsed at follow-up. The adjusted odds ratios (aOR) for predicting relapse from week 9–26 were 1.74 per point increase (95% CI = 1.05–2.89) for TSU and 1.59 (95% CI = 1.11–2.28) for SUT. The aORs for predicting relapse from week 9–52 were 2.41 (95% CI = 1.33–4.37) and 1.71 (95% CI = 1.14–2.56), respectively. Applying a cut-point of ≥3 on TSU resulted in SP = 97.1 and PPV = 70.0 in week 9–26, and SP = 98.8 and PPV = 90.0 in week 9–52. Applying a cut-point of ≥4 on SUT resulted in SP = 99.0 and PPV = 85.7 in week 9–26, and SP = 98.8 and PPV = 85.7 in week 9–52. Both TSU and SUT were valid predictors of long-term relapse in patients under smoking cessation treatment in primary care. These simple questions may be useful to implement in primary care.Trial registration: Dutch Trial Register (NTR3067).

Long-term changes in delay discounting following a smoking cessation treatment for patients with depression

2020 ◽  
Vol 212 ◽  
pp. 108007
Author(s):  
Ángel García-Pérez ◽  
Guillermo Vallejo-Seco ◽  
Sara Weidberg ◽  
Alba González-Roz ◽  
Roberto Secades-Villa
Keyword(s):  
Smoking Cessation ◽  
Delay Discounting ◽  
Smoking Cessation Treatment ◽  
Cessation Treatment ◽  
Long Term Changes

Dopamine transporter polymorphisms are associated with short-term response to smoking cessation treatment

2007 ◽  
Vol 17 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Colin OʼGara ◽  
John Stapleton ◽  
Gay Sutherland ◽  
Camila Guindalini ◽  
Ben Neale ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Dopamine Transporter ◽  
Short Term ◽  
Smoking Cessation Treatment ◽  
Cessation Treatment ◽  
Term Response

scholarly journals An Algorithm Approach to Determining Smoking Cessation Treatment for Persons Living With HIV/AIDS

2015 ◽  
Vol 69 (3) ◽  
pp. 291-298 ◽  
Author(s):  
Karen L. Cropsey ◽  
Bianca F. Jardin ◽  
Greer A. Burkholder ◽  
C. Brendan Clark ◽  
James L. Raper ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Smoking Cessation Treatment ◽  
Cessation Treatment ◽  
Persons Living With Hiv ◽  
Living With Hiv ◽  
Hiv Aids

scholarly journals Cost-effectiveness of a high-intensity versus a low-intensity smoking cessation intervention in a dental setting: long-term follow-up

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e030934 ◽  
Author(s):  
Inna Feldman ◽  
Asgeir Runar Helgason ◽  
Pia Johansson ◽  
Åke Tegelberg ◽  
Eva Nohlert
Keyword(s):  
Smoking Cessation ◽  
Cost Effectiveness ◽  
Incremental Cost ◽  
High Intensity ◽  
Short Term ◽  
Low Intensity ◽  
Cessation Treatment ◽  
Long Term Follow Up

ObjectivesThe aim of this study was to conduct a cost-effectiveness analysis (CEA) of a high-intensity and a low-intensity smoking cessation treatment programme (HIT and LIT) using long-term follow-up effectiveness data and to validate the cost-effectiveness results based on short-term follow-up.Design and outcome measuresIntervention effectiveness was estimated in a randomised controlled trial as numbers of abstinent participants after 1 and 5–8 years of follow-up. The economic evaluation was performed from a societal perspective using a Markov model by estimating future disease-related costs (in Euro (€) 2018) and health effects (in quality-adjusted life-years (QALYs)). Programmes were explicitly compared in an incremental analysis, and the results were presented as an incremental cost-effectiveness ratio.SettingThe study was conducted in dental clinics in Sweden.Participants294 smokers aged 19–71 years were included in the study.InterventionsBehaviour therapy, coaching and pharmacological advice (HIT) was compared with one counselling session introducing a conventional self-help programme (LIT).ResultsThe more costly HIT led to higher number of 6-month continuous abstinent participants after 1 year and higher number of sustained abstinent participants after 5–8 years, which translates into larger societal costs avoided and health gains than LIT. The incremental cost/QALY of HIT compared with LIT amounted to €918 and €3786 using short-term and long-term effectiveness, respectively, which is considered very cost-effective in Sweden.ConclusionCEA favours the more costly HIT if decision makers are willing to spend at least €4000/QALY for tobacco cessation treatment.

scholarly journals Approved and emerging smoking cessation treatments for people with schizophrenia spectrum disorders: A narrative review

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Pasquale Caponnetto ◽  
Riccardo Polosa
Keyword(s):  
Smoking Cessation ◽  
Behavioral Treatment ◽  
Behavioural Therapy ◽  
Schizophrenia Spectrum Disorders ◽  
Smoking Cessation Treatment ◽  
Schizophrenia Spectrum ◽  
Cessation Treatment ◽  
Original Contribution ◽  
Spectrum Disorders

This review focuses on smoking cessation treatments for people with schizophrenia spectrum disorders. It concludes with comments on the significance of the research and why it constitutes an original contribution. We searched PubMed (National Library of Medicine), and PsycINFO (Ovid) (2006-2020) for studies on schizophrenic disorder (schizophrenia or psychotic or psychosis or severe mental illness) and smoking cessation treatment (smoking cessation treatment or varenicline or tobacco cessation or reduction or bupropion or NRT or behavioral treatment or e-cigarette). Studies found evidence suggesting that pharmacotherapy combined with behavioural therapy for smoking cessation is effective amongst smokers with schizophrenia spectrum disorders, although more long-term research is required. This review summarised and critically reviewed also studies on vaping as a smoking cessation strategy for smokers with schizophrenia spectrum disorders and evidence suggests that they may effective as smoking cessation tool and may be less harmful alternatives to combustible cigarette smoking. Consequently, e-cigarettes could be considered as an applicable instrument for Tobacco Harm Reduction (THR) and smoking cessation. Overall, there are very few studies of e-cigarettes for smoking cessation in patients with schizophrenia and these studies are very small. They have promising results, but more research is needed.

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