Program for lung cancer screening and tobacco cessation: Study protocol of a sequential, multiple assignment, randomized trial.
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SUBMITTER: Fu SS
PROVIDER: S-EPMC5558455 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
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Program for lung cancer screening and tobacco cessation: Study protocol of a sequential, multiple assignment, randomized trial.
Fu Steven S SS Rothman Alexander J AJ Vock David M DM Lindgren Bruce B Almirall Daniel D Begnaud Abbie A Melzer Anne A Schertz Kelsey K Glaeser Susan S Hammett Patrick P Joseph Anne M AM
Contemporary clinical trials 20170704
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Project description:ImportanceNearly half of the 14.8 million US adults eligible for lung cancer screening (LCS) smoke cigarettes. The optimal smoking cessation program components for the LCS setting are unclear.ObjectiveTo assess the effect of adding a referral to prescription medication therapy management (MTM) to the tobacco longitudinal care (TLC) program among patients eligible for LCS who smoke and do not respond to early tobacco treatment and to assess the effect of decreasing the intensity of TLC among participants who do respond to early treatment.Design, setting, and participantsThis randomized clinical trial included patients who currently smoked cigarettes daily and were eligible for LCS. Recruitment took place at primary care centers and LCS programs at 3 large health systems in the US and began in October 2016, and 18-month follow-up was completed April 2021.Interventions(1) TLC comprising intensive telephone coaching and combination nicotine replacement therapy for 1 year with at least monthly contact; (2) TLC with MTM, MTM offered pharmacist-referral for prescription medications; and (3) Quarterly TLC, intensity of TLC was decreased to quarterly contact. Intervention assignments were based on early response to tobacco treatment (abstinence) that was assessed either 4 weeks or 8 weeks after treatment initiation.Main outcomes and measuresSelf-reported, 6-month prolonged abstinence at 18-month.ResultsOf 636 participants, 228 (35.9%) were female, 564 (89.4%) were White individuals, and the median (IQR) age was 64.3 (59.6-68.8) years. Four weeks or 8 weeks after treatment initiation, 510 participants (80.2%) continued to smoke (ie, early treatment nonresponders) and 126 participants (19.8%) had quit (ie, early treatment responders). The 18 month follow-up survey response rate was 83.2% (529 of 636). Across TLC groups at 18 months follow-up, the overall 6-month prolonged abstinence rate was 24.4% (129 of 529). Among the 416 early treatment nonresponders, 6-month prolonged abstinence for TLC with MTM vs TLC was 17.8% vs 16.4% (adjusted odds ratio [aOR] 1.13; 95% CI, 0.67-1.89). In TLC with MTM, 98 of 254 participants (39%) completed at least 1 MTM visit. Among 113 early treatment responders, 6-month prolonged abstinence for Quarterly TLC vs TLC was 24 of 55 (43.6%) vs 34 of 58 (58.6%) (aOR, 0.54; 95% CI, 0.25-1.17).Conclusions and relevanceIn this randomized clinical trial, adding referral to MTM with TLC for participants who did not respond to early treatment did not improve smoking abstinence. Stepping down to Quarterly TLC among early treatment responders is not recommended. Integrating longitudinal tobacco cessation care with LCS is feasible and associated with clinically meaningful quit rates.Trial registrationClinicalTrials.gov Identifier: NCT02597491.
| S-EPMC10450571 | biostudies-literature
Project description:BackgroundTobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes.MethodsThis study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs (N = 33 clinics; N = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching.DiscussionThis project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations.Trial registrationThis trial was registered at ClinicalTrials.gov (NCT03900767) on April 4th, 2019.
| S-EPMC6993416 | biostudies-literature
Project description:Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well-known SMARTs to date.
| S-EPMC4184954 | biostudies-literature
Project description:BackgroundAlthough lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients, most of those eligible are not referred for screening. Tobacco cessation counseling may be an opportune time to educate people about LCS, but little is known about the utilization and perceptions of LCS among people undergoing tobacco cessation treatment.Materials and methodsWe surveyed 185 current smokers, including 122 smokers between the ages of 55 and 80 years, who were attending a tobacco cessation class in a large integrated health care system regarding lung cancer risk perception and perceived benefits, harms, and barriers to LCS. We analyzed results according to whether participants had already undergone LCS and also whether they had undergone colorectal cancer screening.ResultsA minority (18.9%) of participants had undergone LCS, and no participant who had not undergone LCS was familiar with LCS. Perceived lung cancer risk was high, and screening was believed to be beneficial. Common barriers included being a current smoker (56.6%), worrying about test results (52.5%), lack of knowledge about the test (50.8%), absence of symptoms of lung cancer (40.2%), costs of the study (35.2%), and worrying about being blamed for having smoked (33.6%). Perceived risk and barriers to LDCT were similar among people who had or had not previously undergone colorectal cancer screening.ConclusionsUtilization of LCS was low, and few smokers were aware of LDCT for LCS. A number of patient-related barriers to screening exist among smokers. Tobacco cessation counseling may be an opportune time to provide education regarding LCS with LDCT.
| S-EPMC8817898 | biostudies-literature
Project description:Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this article, we deal with the design of a treatment program of dynamic treatment regimens (DTRs) for patients with depression post-acute coronary syndrome. We introduce a novel adaptive randomization scheme for a sequential multiple assignment randomized trial of DTRs. Our approach adapts the randomization probabilities to favor treatment sequences having comparatively superior Q-functions used in Q-learning. The proposed approach addresses three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. We demonstrate how to apply our method to design a depression treatment program using data from a previous study. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learning of the treatments by alleviating the curse of dimensionality.
| S-EPMC4429017 | biostudies-literature
Project description:BackgroundStepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components.ObjectiveThe primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss.Study designFour hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m2 will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving <0.5 lb. weight loss on average per week, assessed by wireless scale at 2, 4, and 8 weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12 months.SignificanceResults will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.
| S-EPMC6624080 | biostudies-literature
Project description:BackgroundMobile health (mHealth) is promising in developing personalised smoking cessation interventions. By using an adaptive trial design, we aim to evaluate the effectiveness of personalised mHealth intervention in increasing smoking cessation.MethodsThis study is a two-arm, parallel, accessor-blinded Sequential Multiple-Assignment Randomised Trial (SMART) that randomises 1200 daily cigarette smokers from 70 community sites at two timepoints. In the first phase, participants receive brief cessation advice plus referral assistance to smoking cessation services and are randomly allocated to receive personalised instant messaging (PIM) or regular instant messaging (RIM). In the second phase, PIM participants who are non-responders (i.e. still smoking at 1 month) are randomised to receive either optional combined interventions (multi-media messages, nicotine replacement therapy sampling, financial incentive for active referral, phone counselling, and family/peer support group chat) or continued-PIM. Non-responders in the RIM group are randomised to receive PIM or continued-RIM. Participants who self-report quitting smoking for 7 days or longer at 1 month (responders) in both groups continue to receive the intervention assigned in phase 1. The primary outcomes are biochemical abstinence validated by exhaled carbon monoxide (< 4 ppm) and salivary cotinine (< 10 ng/ml) at 3 and 6 months from treatment initiation. Intention-to-treat analysis will be adopted.DiscussionThis is the first study using a SMART design to evaluate the effect of adaptive mHealth intervention on abstinence in community-recruited daily smokers. If found effective, the proposed intervention will inform the development of adaptive smoking cessation treatment and benefits smokers non-responding to low-intensity mHealth support.Trial registrationClinicalTrials.gov NCT03992742 . Registered on 20 June 2019.
| S-EPMC9387009 | biostudies-literature
Project description:IntroductionPhysical activity monitors, motivational text messages, personal calls, and group meetings, have proven to be efficacious physical activity interventions. However, individual participant response to these interventions varies drastically. A SMART design (sequential multiple assignment randomized trial) provides an effective way to test interventions that start with an initial treatment and then transition to an augmented treatment for non-responders. We describe a SMART to determine the most effective adaptive intervention to increase physical activity (steps, moderate-to-vigorous physical activity) and improve cardiovascular health among employed women who are not regularly physically active. The SMART uses combinations of four treatments: 1) enhanced physical activity monitor (Fitbit wearable activity monitor and mobile app with goal setting and physical activity prescription), 2) text messages, 3) personal calls, and 4) group meetings.MethodsParticipants (N = 312) include women ages 18-70 employed at a large academic medical center. Women will be randomized to an initial intervention, either an enhanced physical activity monitor or enhanced physical activity monitor + text messaging. Non-responders to the initial intervention at 2 months will be randomized to either personal calls or groups meetings for the next 6 months. At 8 months, all participants will return to only an enhanced physical activity monitor until their final 12-month assessment.DiscussionResults of this study will add to the literature on improving physical activity in employed women. This study will identify effective interventions for women who respond to less intensive treatments, while maximizing benefits for those who need a more intensive approach.
| S-EPMC7242143 | biostudies-literature
Project description:AimResearch has shown that preventative intervention in individuals at ultra-high risk of psychosis (UHR) improves symptomatic and functional outcomes. The staged treatment in early psychosis (STEP) trial aims to determine the most effective type, timing and sequence of interventions in the UHR population by sequentially studying the effectiveness of (1) support and problem solving, (2) cognitive-behavioural case management and (3) antidepressant medication with an embedded fast-fail option of (4) omega-3 fatty acids or low-dose antipsychotic medication. This paper presents the recruitment flow and baseline clinical characteristics of the sample.MethodsSTEP is a sequential multiple assignment randomized trial. We present the baseline demographics, clinical characteristics and acceptability and feasibility of this treatment approach as indicated by the flow of participants from first contact up until enrolment into the trial. Recruitment took place between April 2016 and January 2019.ResultsOf 1343, help-seeking young people who were considered for participation, 402 participants were not eligible and 599 declined/disengaged, resulting in a total of 342 participants enrolled in the study. The most common reason for exclusion was an active prescription of antidepressant medication. Eighty-five percent of the enrolled sample had a non-psychotic DSM-5 diagnosis and symptomatic/functional measures showed a moderate level of clinical severity and functional impairment.DiscussionThe present study demonstrates the acceptability and participant's general positive appraisal of sequential treatment. It also shows, in line with other trials in UHR individuals, a significant level of psychiatric morbidity and impairment, demonstrating the clear need for care in this group and that treatment is appropriate.
| S-EPMC9795376 | biostudies-literature
Project description:Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
| S-EPMC7061067 | biostudies-literature