Dataset Information

Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness.

ABSTRACT:

Background

Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role.

Methods

We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics.

Results

For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland.

Conclusion

In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.

SUBMITTER: Huang W

PROVIDER: S-EPMC8297792 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPeople with a serious mental illness are more likely to smoke more and to be more dependent smokers than the general population. This may be due to a wide range of factors that could include a common aetiology to both smoking and the illness, self medication, smoking to alleviate adverse effects of medications, boredom in the existing environment, or a combination of these factors. It is important to undertake this review to facilitate improvements in both the health and safety of people with serious mental illness who smoke, and to reduce the overall burden of costs (both financial and health) to the smoker and, eventually, to the taxpayer.ObjectivesTo review the effects of smoking cessation advice for people with serious mental illness.Search methodsWe searched the Cochrane Schizophrenia Group Specialized Trials Register up to 2 April 2015, which is based on regular searches of CENTRAL, BIOSIS, PubMed, MEDLINE, EMBASE, CINAHL, PsycINFO, and trial registries. We also undertook unsystematic searches of a sample of the component databases (BNI, CINHAL, EMBASE, MEDLINE, and PsycINFO), up to 2 April 2015, and searched references of all identified studiesSelection criteriaWe planned to include all randomised controlled trials (RCTs) that focussed on smoking cessation advice versus standard care or comparing smoking cessation advice with other more focussed methods of delivering care or information.Data collection and analysisThe review authors (PK, AC, and DB) independently screened search results but did not identify any trials that fulfilled the inclusion criteria of this review.Main resultsWe did not identify any RCTs that evaluated advice regarding smoking cessation for people with serious mental illness. The excluded studies illustrate that randomisation of packages of care relevant to smokers with serious mental illness is possible.Authors' conclusionsPeople with serious mental illness are more likely to smoke than the general population. Yet we could not find any high quality evidence to guide the smoking cessation advice healthcare professionals pass onto service users. This is an area where trials are possible and needed.

Project description:Introduction: Individuals with serious mental illness (SMI) smoke at rates two to three times greater than the general population but are less likely to receive treatment. Increasing our understanding of correlates of smoking cessation behaviors in this group can guide intervention development.Aims and methods: Baseline data from an ongoing trial involving smokers with SMI (N = 482) were used to describe smoking cessation behaviors (ie, quit attempts, quit motivation, and smoking cessation treatment) and correlates of these behaviors (ie, demographics, attitudinal and systems-related variables).Results: Forty-three percent of the sample did not report making a quit attempt in the last year, but 44% reported making one to six quit attempts; 43% and 20%, respectively, reported wanting to quit within the next 6 months or the next 30 days. Sixty-one percent used a smoking cessation medication during their quit attempt, while 13% utilized counseling. More quit attempts were associated with lower nicotine dependence and carbon monoxide and greater beliefs about the harms of smoking. Greater quit motivation was associated with lower carbon monoxide, minority race, benefits of cessation counseling, and importance of counseling within the clinic. A greater likelihood of using smoking cessation medications was associated with being female, smoking more cigarettes, and receiving smoking cessation advice. A greater likelihood of using smoking cessation counseling was associated with being male, greater academic achievement, and receiving smoking cessation advice.Conclusions: Many smokers with SMI are engaged in efforts to quit smoking. Measures of smoking cessation behavior are associated with tobacco use indicators, beliefs about smoking, race and gender, and receiving cessation advice.Implications: Consideration of factors related to cessation behaviors among smokers with SMI continues to be warranted, due to their high smoking rates compared to the general population. Increasing our understanding of these predictive characteristics can help promote higher engagement in evidence-based smoking cessation treatments among this subpopulation.

Project description:BACKGROUND:Smoking rates of Australians with severe mental illness (SMI) are disproportionately higher than the general population. Despite the rapid growth in mobile health (mHealth) apps, limited evidence exists to inform their design for SMI populations. OBJECTIVE:This study aimed to explore the feasibility, acceptability, and utility of adapting a novel smoking cessation app (Kick.it) to assist smokers with SMI to prevent smoking relapse and quit. METHODS:Using co-design, two in-depth interviews with 12 adult smokers and ex-smokers with SMI were conducted in this qualitative study. Stage 1 interviews explored participants' smoking-related experiences and perceptions of social support for smoking cessation, informed the development of the stage 2 interview schedule, and provided context for participants' responses to the second interview. Stage 2 interviews explored participants' perceptions of the feasibility, utility, and acceptability of the app features for SMI populations. RESULTS:People with SMI perceived mHealth interventions to support their quit smoking attempts as feasible, acceptable, and useful. Key emerging themes included personalization of the app to users' psychosocial needs, a caring app to mediate self-esteem and self-efficacy, an app that normalizes smoking relapse and multiple quit attempts, a strong focus on user experience to improve usability, and a social network to enhance social support for smoking cessation. CONCLUSIONS:This study gained an in-depth understanding of the lived experiences of smoking and quitting among people with SMI and their perception of the Kick.it app features to help inform the tailoring of the app. Specific program tailoring is required to assist them in navigating the complex interactions between mental illness and smoking in relation to their psychosocial well-being and capacity to quit. This study describes the adaptations required for the Kick.it app to meet the specific needs and preferences of people with SMI. Results of this study will guide the tailoring of the Kick.it app for SMI populations. The study findings can also inform a co-design process for the future development and design of smoking cessation apps for SMI populations.

Project description:IntroductionHigh rates of tobacco use among people with serious mental illness (SMI), along with their unique needs, suggest the importance of developing tailored smoking cessation interventions for this group. Previous early-phase work empirically validated the design and content of Learn to Quit, a theory-based app designed for this population.MethodsIn a pilot randomized controlled trial, we compared the feasibility, acceptability, and preliminary efficacy of Learn to Quit versus QuitGuide, an app designed for the general population. All participants received nicotine replacement therapy and technical assistance. Daily smokers with SMI (N = 62) participated in the trial with outcomes assessed at weeks 4, 8, 12, and 16.ResultsCompared to QuitGuide, Learn to Quit participants had similar number of days of app use (34 vs. 32, p = .754), but larger number of app interactions (335 vs. 205; p = .001), longer durations of app use (4.24 hrs. vs. 2.14 hrs; p = .044), and higher usability scores (85 vs. 79, p = .046). At week 16, Learn to Quit led to greater reductions in cigarettes per day (12.3 vs. 5.9 for QuitGuide; p = 0.10). Thirty-day point prevalence abstinence was verified in 12% of Learn to Quit participants versus 3% of QuitGuide participants (odds ratio = 3.86, confidence interval = 0.41 to 36, p = .239). Changes in psychiatric symptoms and adverse events were not clinically significant between conditions.ConclusionsThis pilot trial provides strong evidence of Learn to Quit's usability, feasibility, and safety. Preliminary evidence suggests the app may be efficacious. A randomized controlled efficacy trial is needed to test the app in a larger sample of smokers with SMI.ImplicationsThis study suggests that the Learn to Quit app is a feasible approach to deliver smoking cessation treatment in patients with co-occurring tobacco use disorder and SMI. This means that, if found efficacious, this technology could be used to deploy smoking cessation treatment to larger segments of this population, hence improving public health. Therefore, a randomized controlled trial should be conducted to examine the efficacy of this digital intervention.

Project description:BackgroundRates of smoking among those with serious mental illness (SMI) are two to three times higher than for the general population. Smoking is rarely addressed in mental health settings. Innovative outreach and treatment strategies are needed to address these disparities. The current study is a pilot study of the feasibility and acceptability of a chronic care model of tobacco cessation treatment implemented in outpatient psychiatry clinics.MethodsParticipants were recruited from two outpatient psychiatric clinics and randomly assigned to intervention (counseling and nicotine replacement for 8 weeks, plus ongoing proactive outreach calls inviting reengagement in treatment) or control (brief education and referral to the state quit line). Assessments were conducted at 8 weeks (end of initial treatment block) and 6 months (end of window for retreatment). Feasibility was assessed by enrollment rate, treatment engagement, and completion of follow-up assessments. Acceptability was assessed both quantitatively and qualitatively. Preliminary efficacy was assessed by 7-day and 30-day abstinence rates, rate of quit attempts, and cigarettes per day. Psychological health was measured to assess for changes related to treatment group or attempts to quit smoking.ResultsNineteen participants were randomized to intervention and 19 to control. Recruitment proved feasible, and high rates of treatment engagement (mean of 4.5 sessions completed in initial treatment block, 89.5% uptake of nicotine replacement) and retention (94.7% of follow-up assessments completed) were observed. Treatment acceptability was high. As anticipated, there were no significant differences in abstinence between groups, but results generally favored the intervention group, including bio-verified 7-day abstinence rates of 21.1% in intervention vs. 17.6% in control and self-reported 30-day abstinence rates of 16.1% in intervention vs. 5.1% in control at 8 weeks. Significantly more intervention participants made at least one quit attempt (94.7% vs 52.6%; OR = 16.20, 95% CI: 1.79-147.01). Cigarettes per day decreased significantly more in the intervention group at 8 weeks (b = - 13.19, SE = 4.88, p = .02).ConclusionsIt was feasible to recruit and retain SMI patients in a smoking cessation trial in the context of outpatient psychiatry. The novel chronic care model treatment was acceptable to patients and showed promise for efficacy. If efficacious, a chronic care model could be effective at reducing smoking among SMI patients.Trial registrationClinicalTrial.gov #: NCT03822416 (registered January 30th 2019).

Project description:BACKGROUND:Smoking rates in the United States have been reduced in the past decades to 15% of the general population. However, up to 88% of people with psychiatric symptoms still smoke, leading to high rates of disease and mortality. Therefore, there is a great need to develop smoking cessation interventions that have adequate levels of usability and can reach this population. OBJECTIVE:The objective of this study was to report the rationale, ideation, design, user research, and final specifications of a novel smoking cessation app for people with serious mental illness (SMI) that will be tested in a feasibility trial. METHODS:We used a variety of user-centered design methods and materials to develop the tailored smoking cessation app. This included expert panel guidance, a set of design principles and theory-based smoking cessation content, development of personas and paper prototyping, usability testing of the app prototype, establishment of app's core vision and design specification, and collaboration with a software development company. RESULTS:We developed Learn to Quit, a smoking cessation app designed and tailored to individuals with SMI that incorporates the following: (1) evidence-based smoking cessation content from Acceptance and Commitment Therapy and US Clinical Practice Guidelines for smoking cessation aimed at providing skills for quitting while addressing mental health symptoms, (2) a set of behavioral principles to increase retention and comprehension of smoking cessation content, (3) a gamification component to encourage and sustain app engagement during a 14-day period, (4) an app structure and layout designed to minimize usability errors in people with SMI, and (5) a set of stories and visuals that communicate smoking cessation concepts and skills in simple terms. CONCLUSIONS:Despite its increasing importance, the design and development of mHealth technology is typically underreported, hampering scientific innovation. This report describes the systematic development of the first smoking cessation app tailored to people with SMI, a population with very high rates of nicotine addiction, and offers new design strategies to engage this population. mHealth developers in smoking cessation and related fields could benefit from a design strategy that capitalizes on the role visual engagement, storytelling, and the systematic application of behavior analytic principles to deliver evidence-based content.

Project description:Overweight and obesity are epidemic among persons with serious mental illness, yet weight-loss trials systematically exclude this vulnerable population. Lifestyle interventions require adaptation in this group because psychiatric symptoms and cognitive impairment are highly prevalent. Our objective was to determine the effectiveness of an 18-month tailored behavioral weight-loss intervention in adults with serious mental illness.We recruited overweight or obese adults from 10 community psychiatric rehabilitation outpatient programs and randomly assigned them to an intervention or a control group. Participants in the intervention group received tailored group and individual weight-management sessions and group exercise sessions. Weight change was assessed at 6, 12, and 18 months.Of 291 participants who underwent randomization, 58.1% had schizophrenia or a schizoaffective disorder, 22.0% had bipolar disorder, and 12.0% had major depression. At baseline, the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 36.3, and the mean weight was 102.7 kg (225.9 lb). Data on weight at 18 months were obtained from 279 participants. Weight loss in the intervention group increased progressively over the 18-month study period and differed significantly from the control group at each follow-up visit. At 18 months, the mean between-group difference in weight (change in intervention group minus change in control group) was -3.2 kg (-7.0 lb, P=0.002); 37.8% of the participants in the intervention group lost 5% or more of their initial weight, as compared with 22.7% of those in the control group (P=0.009). There were no significant between-group differences in adverse events.A behavioral weight-loss intervention significantly reduced weight over a period of 18 months in overweight and obese adults with serious mental illness. Given the epidemic of obesity and weight-related disease among persons with serious mental illness, our findings support implementation of targeted behavioral weight-loss interventions in this high-risk population. (Funded by the National Institute of Mental Health; ACHIEVE ClinicalTrials.gov number, NCT00902694.).