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The purpose of this study is to validate the seven-item wheezing module from the International Study of Asthma and Allergies in Children (ISAAC) in the nationally representative Population Assessment of Tobacco and Health Study. Adult participants with complete Wave 2-3 data were selected, including those with asthma but excluding those with COPD and other respiratory diseases (n = 16,295). We created a nine-point respiratory symptom index from the ISAAC questions, assessed the reliability of the index, and examined associations with self-reported asthma diagnosis. Threshold values were assessed for association with functional outcomes. The weighted prevalence for one or more respiratory symptom was 18.0% (SE = 0.5) for adults without asthma, 70.1% (SE = 1.3) for those with lifetime asthma, 75.7% (SE = 3.7) for adults with past-year asthma not on medications, and 92.6% (SE = 1.6) for those on medications. Cronbach's alpha for the respiratory symptom index was 0.86. Index scores of ≥2 or ≥3 yielded functionally important respiratory symptom prevalence of 7-10%, adequate sensitivity and specificity for identifying asthma, and consistent independent associations with all functional outcomes and tobacco use variables. Respiratory symptom index scores of ≥2 or ≥3 are indicative of functionally important respiratory symptoms and could be used to assess the relationship between tobacco use and respiratory health.

BackgroundSeveral single-site alcohol treatment clinical trials have demonstrated efficacy for immediate-release (IR) gabapentin in reducing drinking outcomes among individuals with alcohol dependence. The purpose of this study was to conduct a large, multisite clinical trial of gabapentin enacarbil extended-release (GE-XR) (HORIZANT® ), a gabapentin prodrug formulation, to determine its safety and efficacy in treating alcohol use disorder (AUD).MethodsMen and women (n = 346) who met DSM-5 criteria for at least moderate AUD were recruited across 10 U.S. clinical sites. Participants received double-blind GE-XR (600 mg twice a day) or placebo and a computerized behavioral intervention (Take Control) for 6 months. Efficacy analyses were prespecified for the last 4 weeks of the treatment period.ResultsThe GE-XR and placebo groups did not differ significantly on the primary outcome measure, percentage of subjects with no heavy drinking days (28.3 vs. 21.5, respectively, p = 0.157). Similarly, no clinical benefit was found for other drinking measures (percent subjects abstinent, percent days abstinent, percent heavy drinking days, drinks per week, drinks per drinking day), alcohol craving, alcohol-related consequences, sleep problems, smoking, and depression/anxiety symptoms. Common side-effects were fatigue, dizziness, and somnolence. A population pharmacokinetics analysis revealed that patients had lower gabapentin exposure levels compared with those in other studies using a similar dose but for other indications.ConclusionsOverall, GE-XR at 600 mg twice a day did not reduce alcohol consumption or craving in individuals with AUD. It is possible that, unlike the IR formulation of gabapentin, which showed efficacy in smaller Phase 2 trials at a higher dose, GE-XR is not effective in treating AUD, at least not at doses approved by the U.S. Food and Drug Administration for treating other medical conditions.

INTRODUCTION The e-cigarette market is large and diverse. Traditional smoking cessation trials involving a control group and a 6-month observation period are an inefficient methodology for testing the multiple treatment options e-cigarettes provide for harm reduction in cigarette smokers. We determined when product substitution occurred in the e-cigarette provision arm of an e-cigarette substitution trial for cigarette smokers who were not interested in quitting. METHODS We conducted a secondary analysis of 120 cigarette smokers with severe mental illness (recruitment 2017-2020) who were given disposable e-cigarettes for 8 weeks and assessed at weeks 0 (t0), 2, 4, 6, and 8. We explored product substitution through visit-to-visit correlations in change in product use, then developed a dual process growth model for cigarette and e-cigarette use to test the association between increases in e-cigarette use and concurrent decreases in cigarettes smoked. RESULTS Mean age of the participants was 45.9 years, and 42.7% smoked >20 cigarettes per day. Almost all product substitution occurred between t0 and t2. For the average smoker (18 cigarettes per day), t2 cigarette frequency decreased by 0.39 (95% CI: -0.56 - -0.22) cigarettes for each additional e-cigarette session. There was effect modification (p=0.033), such that baseline light smokers (<10 cigarettes/day) had no significant decrease in t2 cigarette frequency, regardless of their initial increase in e-cigarette use, while heavy smokers (38 cigarettes/day) switched products nearly on a one-to-one basis. CONCLUSIONS In this study, most product substitution occurred early, and heavier smokers had larger t2 decreases in cigarettes/day with increased e-cigarette use. If confirmed with replication studies, the findings could suggest establishment of a novel outcome for e-cigarette studies - early product substitution - and support the value of short-term comparative effectiveness trials that compare multiple potentially lower harm tobacco products. [ABSTRACT FROM AUTHOR]

Digital therapeutics can overcome many of the barriers to translation of evidence-based treatment for substance use, mental health, and other behavioral health conditions. Delivered via nearly ubiquitous platforms such as the web, smartphone applications, text messaging, and videoconferencing, digital therapeutics can transcend the time and geographic boundaries of traditional clinical settings so that individuals can access care when and where they need it. There is strong empirical support for digital therapeutic approaches for behavioral health, yet implementation science with regard to scaling use of digital therapeutics for behavioral health is still in its early stages. In this paper, we summarize the proceedings of a day-long workshop, “Implementation Science and Digital Therapeutics,” sponsored and hosted by the Center for Technology and Behavioral Health at Dartmouth College. The Center for Technology and Behavioral Health is an interdisciplinary P30 Center of Excellence funded by the National Institute on Drug Abuse, with the mission of promoting state-of-the-technology and state-of-the-science for the development, evaluation, and sustainable implementation of digital therapeutic approaches for substance use and related conditions. Workshop presentations were grounded in current models of implementation science. Directions and opportunities for collaborative implementation science research to promote broad adoption of digital therapeutics for behavioral health are offered.

Introduction Schizophrenia and schizoaffective disorder are heterogeneous psychotic disorders that often cause significant disability, with symptoms that include delusions, hallucinations, disorganization, and cognitive impairment. (1) In schizoaffective disorder, the psychotic [...]

BackgroundYoung adults with serious mental illness are over twice as likely to have tobacco use disorder than those in the general population and are less likely to utilize proven treatment methods during quit attempts. However, little research has evaluated the efficacy of interventions for this group. Smartphone apps may be an underutilized tool for tobacco use disorder among young adults with serious mental illness. ObjectiveThe aim of this study was to explore attitudes toward smoking cessation apps and preferences regarding app design in young adult smokers with serious mental illness. MethodsFive focus groups involving 25- to 35-year-old adults with serious mental illness receiving treatment at a community mental health center were conducted between May 2019 and August 2019. Three researchers independently coded transcripts and identified themes using thematic analysis. ResultsParticipants (n=22) were individuals who smoke daily: 10 (46%) self-identified as female, 18 (82%) self-identified as White, and 9 (41%) had psychotic disorders. Key themes that emerged included a general interest in using health apps; a desire for apps to provide ongoing motivation during a quit attempt via social support, progress tracking, and rewards; a desire for apps to provide distraction from smoking; concerns about app effectiveness due to a lack of external accountability; and concerns that apps could trigger cravings or smoking behavior by mentioning cigarettes or the act of smoking. ConclusionsApps have the potential to support smoking cessation or reduction efforts among young adults with serious mental illness. However, they may require tailoring, optimization, and clinical support to effectively promote cessation in this population.

BackgroundIn-person motivational interventions increase engagement with evidence-based cessation treatments among smokers with schizophrenia, but access to such interventions can be limited because of workforce shortages and competing demands in mental health clinics. The use of digital technology to deliver interventions can increase access, but cognitive impairments in schizophrenia may impede the use of standard digital interventions. We developed an interactive, multimedia, digital motivational decision support system for smokers with schizophrenia (Let’s Talk About Smoking). We also digitalized a standard educational pamphlet from the National Cancer Institute (NCI Education). Both were tailored to reduce cognitive load during use. ObjectiveWe conducted a randomized trial of Let’s Talk About Smoking versus NCI Education to test whether the interactive motivational intervention was more effective and more appealing than the static educational intervention for increasing use of smoking cessation treatment, quit attempts, and abstinence among smokers with schizophrenia, accounting for the level of cognitive functioning. MethodsAdult smokers with schizophrenia (n=162) were enrolled in the study from 2014 to 2015, randomly assigned to an intervention condition, and assessed in person at 3- and 6-month follow-ups. Interventions were delivered on a laptop computer in a single session. All participants had access to standard, community-delivered cessation treatments during follow-up. Multivariate models were used to evaluate outcomes. ResultsTreatment initiation outcomes were not different between intervention conditions (27/84 [32%] for Let’s Talk About Smoking vs 36/78 [46%] for NCI Education; odds ratio [OR] 0.71 [95% CI 0.37-1.33]); 38.9% (63/162) of participants initiated treatment. Older age (OR 1.03 [95% CI 1.00-1.07]; P=.05), higher education (OR 1.21 [95% CI 1.04-1.41]; P=.03), and fewer positive symptoms (OR 0.87 [95% CI 0.80-0.96]; P=.01) predicted cessation treatment initiation, whereas level of cognition did not. The mean satisfaction and usability index score was higher for Let’s Talk About Smoking versus NCI Education (8.9 [SD 1.3] vs 8.3 [SD 2.1]; t120.7=2.0; P=.045). Quit attempts (25/84, 30% vs 36/78, 46%; estimate [Est]=−0.093, SE 0.48; P=.85) and abstinence (1/84, 1% vs 6/78, 7%; χ21=3.4; P=.07) were not significantly different between intervention conditions. Cognitive functioning at baseline (Est=1.47, SE 0.47; P=.002) and use of any behavioral or medication cessation treatment (Est=1.43, SE 0.47; P=.003) predicted quit attempts with self-reported abstinence over the 6-month follow-up. ConclusionsThe interactive, multimedia intervention was not more effective than the static, text-based intervention among smokers with schizophrenia. Both tailored digital interventions resulted in levels of treatment engagement and quit attempts that were similar to findings from previous studies of in-person interventions, confirming the potential role of digital interventions to educate and motivate smokers with schizophrenia to use cessation treatment and to quit smoking. These findings indicate that additional cessation treatment is needed after brief education or motivational interventions, and that cessation treatment should be adjusted for people with cognitive impairment. Trial RegistrationClinicalTrials.gov NCT02086162; https://clinicaltrials.gov/show/NCT02086162

The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.

Background: Psychiatric hospitalization is a major driver of cost in the treatment of schizophrenia. Here, we asked whether a technology-enhanced approach to relapse prevention could reduce days spent in a hospital after discharge. Methods: The Improving Care and Reducing Cost (ICRC) study was a quasi-experimental clinical trial in outpatients with schizophrenia conducted between 26 February 2013 and 17 April 2015 at 10 different sites in the USA in an outpatient setting. Patients were between 18 and 60 years old with a diagnosis of schizophrenia, schizoaffective disorder, or psychotic disorder not otherwise specified. Patients received usual care or a technology-enhanced relapse prevention program during a 6-month period after discharge. The health technology program included in-person, individualized relapse prevention planning with treatments delivered via smartphones and computers, as well as a web-based prescriber decision support program. The main outcome measure was days spent in a psychiatric hospital during 6 months after discharge. Results: The study included 462 patients, of which 438 had complete baseline data and were thus used for propensity matching and analysis. Control participants (N = 89; 37 females) were enrolled first and received usual care for relapse prevention followed by 349 participants (128 females) who received technology-enhanced relapse prevention. During 6-month follow-up, 43% of control and 24% of intervention participants were hospitalized (χ2 = 11.76, p<0.001). Days of hospitalization were reduced by 5 days (mean days: b = −4.58, 95% CI −9.03 to −0.13, p = 0.044) in the intervention condition compared to control. Conclusions: These results suggest that technology-enhanced relapse prevention is an effective and feasible way to reduce rehospitalization days among patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Many people learn about smoking cessation through information on the Internet. Whether people with severe mental illnesses, who have very high rates of smoking, are able to use currently available websites about smoking cessation is unknown. The study reported here assessed whether four smoking cessation websites met usability guidelines and whether they were usable by smokers with severe mental illnesses. Four websites that appeared first on a Google search and represented an array of sponsors were selected. First, five experts rated the websites on adequacy of content in six areas and usability in 20 areas. Second, 16 smokers with severe mental illnesses performed two search tasks on the websites with researchers observing their searches and interviewing them regarding usability. One of the websites was rated by experts as acceptable for content and usability, but most of the participants were unable to navigate this website. The only website that was navigable received poor content ratings by experts. Four easily accessible websites did not meet the needs of smokers with severe mental illnesses. Although the Internet is a promising strategy to provide education about treatments, website developers must attend to the needs and capacities of multiple user groups.