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Introduction: Approved pharmacological treatments for smoking cessation are modestly effective, underscoring the need for improved pharmacotherapies. Glucagon-like peptide-1 receptor (GLP-1R) agonists attenuate the rewarding effects of nicotine in preclinical studies. We examined the efficacy of extended-release exenatide, a GLP-1R agonist, combined with nicotine replacement therapy (NRT, patch) for smoking cessation, craving, and withdrawal symptoms, with post-cessation body weight as a secondary outcome.Methods: Eighty-four prediabetic and/or overweight smokers were randomized (1 : 1) to once-weekly placebo or exenatide, 2 mg, subcutaneously. All participants received NRT (21 mg) and brief smoking cessation counseling. Seven-day point prevalence abstinence (expired CO level ≤5 ppm), craving, withdrawal, and post-cessation body weight were assessed following 6 weeks of treatment. A Bayesian approach for analyzing generalized linear models yielded posterior probabilities (PP) to quantify the evidence favoring hypothesized effects of treatment on the study outcomes.Results: Exenatide increased the risk for smoking abstinence compared to placebo (46.3% and 26.8%, respectively), (risk ratio [RR] = 1.70; 95% credible interval = [0.96, 3.27]; PP = 96.5%). Exenatide reduced end-of-treatment craving in the overall sample and withdrawal among abstainers. Post-cessation body weight was 5.6 pounds lower in the exenatide group compared to placebo (PP = 97.4%). Adverse events were reported in 9.5% and 2.3% of participants in the exenatide and placebo groups, respectively.Conclusions: Exenatide, in combination with the NRT improved smoking abstinence, reduced craving and withdrawal symptoms, and decreased weight gain among abstainers. Findings suggest that the GLP-1R agonist strategy is worthy of further research in larger, longer duration studies.Implications: Despite considerable progress in tobacco control, cigarette smoking remains the leading cause of preventable disease, disability, and death. In this pilot study, we showed that extended-release exenatide, a glucagon-like peptide-1 receptor agonist, added to the nicotine patch, improved abstinence and mitigated post-cessation body weight gain compared to patch alone. Further research is needed to confirm these initial positive results. [ABSTRACT FROM AUTHOR]

Introduction: Thirdhand smoke (THS) is ultrafine particulate matter and residue resulting from tobacco combustion, with implications for health-related harm (eg, impaired wound healing), particularly among hospitalized infants. Project aims were to characterize nicotine (THS proxy) transported on neonatal intensive care unit (NICU) visitors and deposited on bedside furniture, as well as infant exposure.Methods: Cross-sectional data were collected from participants in a metropolitan NICU. Participants completed a survey and carbon monoxide breath sample, and 41.9% (n = 88) of participants (n = 210) were randomly selected for finger-nicotine wipes during a study phase when all bedside visitors were screened for nicotine use and finger-nicotine levels. During an overlapping study phase, 80 mother-infant dyads consented to bedside furniture-nicotine wipes and an infant urine sample (for cotinine analyses).Results: Most nonstaff visitors' fingers had nicotine above the limit of quantification (>LOQ; 61.9%). Almost all bedside furniture surfaces (93.8%) and infant cotinine measures (93.6%) had values >LOQ, regardless of household nicotine use. Participants who reported using (or lived with others who used) nicotine had greater furniture-nicotine contamination (Mdn = 0.6 [interquartile range, IQR = 0.2-1.6] µg/m2) and higher infant cotinine (Mdn = 0.09 [IQR = 0.04-0.25] ng/mL) compared to participants who reported no household-member nicotine use (Mdn = 0.5 [IQR = 0.2-0.7] µg/m2; Mdn = 0.04 [IQR = 0.03-0.07] ng/mL, respectively). Bayesian univariate regressions supported hypotheses that increased nicotine use/exposure correlated with greater nicotine contamination (on fingers/furniture) and infant THS exposure.Conclusions: Potential furniture-contamination pathways and infant-exposure routes (eg, dermal) during NICU hospitalization were identified, despite hospital prohibitions on tobacco/nicotine use. This work highlights the surreptitious spread of nicotine and potential THS-related health risks to vulnerable infants during critical stages of development.Implications: THS contamination is underexplored in medical settings. Infants who were cared for in the NICU are vulnerable to health risks from THS exposure. This study demonstrated that 62% of nonstaff NICU visitors transport nicotine on their fingers to the NICU. Over 90% of NICU (bedside) furniture was contaminated with nicotine, regardless of visitors' reported household-member nicotine use or nonuse. Over 90% of infants had detectable levels of urinary cotinine during NICU hospitalizations. Results justify further research to better protect infants from unintended THS exposure while hospitalized. [ABSTRACT FROM AUTHOR]

Objective The present study examined the effects of home-based remotely supervised transcranial direct current stimulation on quantitative sensory testing measurements in older adults with knee osteoarthritis. Participants were hypothesized to experience improved pain measurements over time. Design Open-label, single-arm trial. Setting Southeast Texas between March and November 2018 at a nursing school and participant homes. Subjects Older adults (aged 50–85 years) with self-reported unilateral or bilateral knee osteoarthritis pain who met eligibility criteria set by the American College of Rheumatology. Methods The intervention was applied with a constant current intensity for 20 minutes every weekday for two weeks (10 total sessions). Quantitative measures of pain were collected three times over 10 days (days 1, 5, and 10) and included heat threshold and tolerance, pressure pain threshold, punctate mechanical pain, pain, and conditioned pain modulation. Analyses used nonparametric tests to evaluate differences between day 1 and day 10. Generalized linear mixed models were then used to evaluate change across all three time points for each measure. Bayesian inference was used to provide the posterior probability of longitudinal effects. Results Nonparametric tests found improvements in seven measures, and longitudinal models supported improvements in 10 measures, with some nonlinear effects. Conclusions The home-based, remotely supervised intervention improved quantitative measurements of pain in older adults with knee osteoarthritis. This study contributes to the growing body of literature supporting home-based noninvasive stimulation interventions. [ABSTRACT FROM AUTHOR]

Introduction: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention.Methods: In a randomized-controlled smoking cessation trial, smartphone-based EMAs were collected from 92 participants following a scheduled quit date. This secondary analysis utilized elastic net-penalized cox proportional hazards regression and model approximation via backward elimination to (1) optimize a predictive model of time to first lapse and (2) simplify that model to its core constituent predictors to maximize parsimony and generalizability.Results: Elastic net proportional hazards regression selected 17 of 26 possible predictors from 2065 EMAs to model time to first lapse. The predictors with the highest magnitude regression coefficients were having consumed alcohol in the past hour, being around and interacting with a smoker, and having cigarettes easily available. This model was reduced using backward elimination, retaining five predictors and approximating to 93.9% of model fit. The retained predictors included those mentioned above as well as feeling irritable and being in areas where smoking is either discouraged or allowed (as opposed to not permitted).Conclusions: The strongest predictors of smoking lapse were environmental in nature (e.g., being in smoking-permitted areas) as opposed to internal factors such as psychological affect. Interventions may be improved by a renewed focus of interventions on these predictors.Implications: The present study demonstrated the utility of machine learning algorithms to optimize the prediction of time to smoking lapse using EMA data. The two models generated by the present analysis found that environmental factors were most strongly related to smoking lapse. The results support the use of machine learning algorithms to investigate intensive longitudinal data, and provide a foundation for the development of highly tailored, just-in-time interventions that can target on multiple antecedents of smoking lapse. [ABSTRACT FROM AUTHOR]