IMPORTANCE Recommendations for chronic pain treatment emphasize multimodal approaches, including nonpharmacologic interventions to enhance self-management. Cognitive behavioral therapy (CBT) is an evidence-based treatment that facilitates management of chronic pain and improves outcomes, but access barriers persist. Cognitive behavioral therapy delivery assisted by health technology can obviate the need for in-person visits, but the effectiveness of this alternative to standard therapy is unknown. The Cooperative Pain Education and Self-management (COPES) trial was a randomized, noninferiority trial comparing IVR-CBT to in-person CBT for patients with chronic back pain. OBJECTIVE To assess the efficacy of interactive voice response-based CBT (IVR-CBT) relative to in-person CBT for chronic back pain. DESIGN, SETTING, AND PARTICIPANTS We conducted a noninferiority randomized trial in 1 Department of Veterans Affairs (VA) health care system. A total of 125 patients with chronic back pain were equally allocated to IVR-CBT (n = 62) or in-person CBT (n = 63). INTERVENTIONS Patients treated with IVR-CBT received a self-help manual and weekly prerecorded therapist feedback based on their IVR-reported activity, coping skill practice, and pain outcomes. In-person CBT included weekly, individual CBT sessions with a therapist. Participants in both conditions received IVR monitoring of pain, sleep, activity levels, and pain coping skill practice during treatment. MAIN OUTCOMES AND MEASURES The primary outcome was change from baseline to 3 months in unblinded patient report of average pain intensity measured by the Numeric Rating Scale (NRS). Secondary outcomes included changes in pain-related interference, physical and emotional functioning, sleep quality, and quality of life at 3, 6, and 9 months. We also examined treatment retention. RESULTS Of the 125 patients (97 men, 28 women; mean [SD] age, 57.9 [11.6] years), the adjusted average reduction in NRS with IVR-CBT (−0.77) was similar to in-person CBT (−0.84), with the 95% CI for the difference between groups (−0.67 to 0.80) falling below the prespecified noninferiority margin of 1 indicating IVR-CBT is noninferior. Fifty-four patients randomized to IVR-CBT and 50 randomized to in-person CBT were included in the analysis of the primary outcome. Statistically significant improvements in physical functioning, sleep quality, and physical quality of life at 3 months relative to baseline occurred in both treatments, with no advantage for either treatment. Treatment dropout was lower in IVR-CBT with patients completing on average 2.3 (95% CI, 1.0-3.6) more sessions. CONCLUSIONS AND RELEVANCE IVR-CBT is a low-burden alternative that can increase access to CBT for chronic pain and shows promise as a nonpharmacologic treatment option for chronic pain, with outcomes that are not inferior to in-person CBT. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01025752

Background Tobacco products containing menthol are widely used by youth. We used ecigarettes to conduct an experimental evaluation of the independent and interactive effects of menthol and nicotine among youth. Procedures Pilot chemosensory experiments with fourteen e-cigarette users identified low (barely perceptible, 0.5%) and high (similar to commercial e-liquid, 3.5%) menthol concentrations. Sixty e-cigarette users were randomized to a nicotine concentration (0 mg/ml, 6 mg/ml, 12 mg/ml) and participated in 3 laboratory sessions. During each session, they received their assigned nicotine concentration, along with one of three menthol concentrations in random counterbalanced order across sessions (0, 0.5%, 3.5%), and participated in three fixed-dose, and an ad-lib, puffing period. Urinary menthol glucuronide and salivary nicotine levels validated menthol and nicotine exposure. We examined changes in e-cigarette liking/wanting and taste, coolness, stimulant effects, nicotine withdrawal and ad-lib use. Results Overall, the high concentration of menthol (3.5%) significantly increased e-cigarette liking/wanting relative to no menthol (p<0.001); there was marginal evidence of nicotine* menthol interactions (p=0.06), with an increase in liking/wanting when 3.5% menthol was combined with 12 mg/ml nicotine, but not 6 mg/ml nicotine. Importantly, both 0.5% and 3.5% menthol concentrations significantly improved taste and increased coolness. We did not observe nicotine or menthol-related changes in stimulant effects, nicotine withdrawal symptoms or ad-lib use. Conclusions Menthol, even at very low doses, alters the appeal of e-cigarettes among youth. Further, menthol enhances positive rewarding effects of high nicotine-containing e-cigarettes among youth.

Objective PTSD increases cardiovascular disease and cardiovascular mortality risk. Neither the prospective relationship of PTSD to incident hypertension risk, nor the effect of PTSD treatment on hypertension risk has been established. Methods Data from a nationally representative sample of 194,319 veterans were drawn from the Veterans Administration roster of United States service men and women. This included veterans whose end of last deployment was from September 2001 – July 2010, and whose first VA medical visit was from October 1, 2001 – January 1, 2009. Incident hypertension was modeled as 3 events: 1) a new diagnosis of hypertension; and/or 2) a new prescription for anti-hypertensive medication; and/or 3) a clinic BP reading in the hypertensive range (≥140mmHg/90mmHg, systolic/diastolic). PTSD diagnosis was the main predictor. PTSD treatment was defined as, 1) at least 8 individual psychotherapy sessions of ≥ 50min during any consecutive 6 months and/or 2) a prescription for SSRI medication. Results Over a median 2.4 year follow-up, the incident hypertension risk independently associated with PTSD ranged from HR=1.12 (95% CI 1.08 – 1.17, p<0.0001) to HR=1.30 (95% CI 1.26 – 1.34, p<0.0001). The interaction of PTSD and treatment revealed that treatment reduced the PTSD associated hypertension risk (e.g., from HR=1.44 [95% CI 1.38 – 1.50, p<0.0001] for those untreated, to HR=1.20 [95% CI 1.15 – 1.25, p<0.0001] for those treated). Conclusions These results indicate that reducing the long term health impact of PTSD and the associated costs, may require very early surveillance and treatment.

We consider situations in Bayesian analysis where we have a family of priors ν h on the parameter θ, where h varies continuously over a space H, and we deal with two related problems. The first involves sensitivity analysis and is stated as follows. Suppose we fix a function f of θ. How do we efficiently estimate the posterior expectation of f (θ) simultaneously for all h in H? The second problem is how do we identify subsets of H which give rise to reasonable choices of ν h ? We assume that we are able to generate Markov chain samples from the posterior for a finite number of the priors, and we develop a methodology, based on a combination of importance sampling and the use of control variates, for dealing with these two problems. The methodology applies very generally, and we show how it applies in particular to a commonly used model for variable selection in Bayesian linear regression, and give an illustration on the US crime data of Vandaele.

Count outcomes are frequent in tobacco research and often have many zeros and exhibit large variance and skew. Analyzing such data based on methods requiring a normally distributed outcome are inappropriate and will likely produce spurious results. This study compares and contrasts appropriate methods for analyzing count data, specifically those with an over-abundance of zeros, and illustrates their use on cigarette and marijuana smoking data. Recommendations are provided.

Background and Aims To estimate progression to polytobacco use (PTU) over 1 year among a sample of US youth. Design Prospective survey with two waves 1 year apart: wave 1 (2013–14) and wave 2 (2014–15). We conducted latent transition analysis (LTA) to identify latent class transitions and examine socio‐demographic predictors of transition types. Setting United States. Participants A total of 11, 996 people who were aged 12–17 years at wave 1. Measurements Publicly available data were used from the Population Assessment of Tobacco and Health (PATH) study, a nationally representative sample of US civilian, non‐institutionalized population aged 12 years and older. Tobacco use status was assessed and classified in terms of: never use, non‐current (not in the past 30 days) and current (past 30‐day) use of cigarettes, cigars, e‐cigarettes, hookah and smokeless tobacco. Other nicotine products were excluded because rates of use were either too low to model (e.g. pipe) or the product was not assessed in the PATH youth sample (e.g. nicotine replacement products). Findings We identified three distinct patterns: class 1, non‐use (wave 1 prevalence = 86%; wave 2 prevalence = 78%); class 2, ever use of cigarettes and e‐cigarettes (wave 1 prevalence = 11%; wave 2 prevalence = 14%); and class 3, current PTU (wave 1 prevalence = 4%; wave 2 prevalence = 7%). Probability of progression from non‐use to ever use of cigarettes and e‐cigarettes was 0.06 and ever use of cigarettes and e‐cigarettes to current PTU was 0.32. Non‐users were more likely to transition to ever use of cigarettes and e‐cigarettes if they were older (versus younger), white (versus non‐white) or if their parental education level was high school or less (versus more than high school); and ever users of cigarettes and e‐cigarettes to current PTU if they were older, male or white. Conclusions US youth who had previously tried e‐cigarettes and cigarettes at wave 1 (2013–14) had a 32% chance of transitioning to current use of two or more tobacco products within 1 year.

The post-deployment period is critical for weight gain, particularly for veterans diagnosed with PTSD and women veterans with PTSD. Efforts are needed to engage post-deployment veterans in weight management services, and to determine whether tailored recruitment/treatment interventions will reduce disparities for veterans with PTSD.

IMPORTANCECognitive behavioral therapy for chronic pain (CBT-CP) is a safe and effective alternative to opioid analgesics. Because CBT-CP requires multiple sessions and therapists are scarce, many patients have limited access or fail to complete treatment.OBJECTIVES To determine if a CBT-CP program that personalizes patient treatment using reinforcement learning, a field of artificial intelligence (AI), and interactive voice response (IVR) calls is noninferior to standard telephone CBT-CP and saves therapist time. DESIGN, SETTING, AND PARTICIPANTSThis was a randomized noninferiority, comparative effectiveness trial including 278 patients with chronic back pain from the Department of Veterans Affairs health system (recruitment and data collection from July 11, 2017-April 9, 2020). More patients were randomized to the AI-CBT-CP group than to the control (1.4:1) to maximize the system's ability to learn from patient interactions.INTERVENTIONS All patients received 10 weeks of CBT-CP. For the AI-CBT-CP group, patient feedback via daily IVR calls was used by the AI engine to make weekly recommendations for either a 45-minute or 15-minute therapist-delivered telephone session or an individualized IVR-delivered therapist message. Patients in the comparison group were offered 10 therapist-delivered telephone CBT-CP sessions (45 minutes/session). MAIN OUTCOMES AND MEASURESThe primary outcome was the Roland Morris Disability Questionnaire (RMDQ; range 0-24), measured at 3 months (primary end point) and 6 months. Secondary outcomes included pain intensity and pain interference. Consensus guidelines were used to identify clinically meaningful improvements for responder analyses (eg, a 30% improvement in RMDQ scores and pain intensity). Data analyses were performed from April 2021 to May 2022. RESULTSThe study population included 278 patients (mean [SD] age, 63.9 [12.2] years; 248 [89.2%] men; 225 [81.8%] White individuals). The 3-month mean RMDQ score difference between AI-CBT-CP and standard CBT-CP was −0.72 points (95% CI, −2.06 to 0.62) and the 6-month difference was -1.24 (95% CI, -2.48 to 0); noninferiority criterion were met at both the 3-and 6-month end points (P < .001 for both). A greater proportion of patients receiving AI-CBT-CP had clinically meaningful improvements at 6 months as indicated by RMDQ (37% vs 19%; P = .01) and pain intensity scores (29% vs 17%; P = .03). There were no significant differences in secondary outcomes. Pain therapy using AI-CBT-CP required less than half of the therapist time as standard CBT-CP. CONCLUSIONS AND RELEVANCEThe findings of this randomized comparative effectiveness trial indicated that AI-CBT-CP was noninferior to therapist-delivered telephone CBT-CP and required substantially less therapist time. Interventions like AI-CBT-CP could allow many more patients to be served effectively by CBT-CP programs using the same number of therapists.