A Gender-Informed Smoking Cessation App for Women: A Protocol for an Acceptability and Feasibility Study.

BACKGROUND: Tobacco smoking remains the leading preventable cause of death and disease among women. Quitting smoking offers numerous health benefits; however, women tend to have less success than men when attempting to quit. This discrepancy is partly due to sex and/or gender-related factors, including the lower effectiveness of smoking cessation medication and the presence of unique motives for smoking and barriers to quitting among women. Despite the gendered nature of smoking, most smoking cessation apps are gender-neutral, and fail to address women's specific needs. OBJECTIVE: To test the acceptability and feasibility of a smartphone app that delivers gender-informed content to support women in quitting smoking. METHODS: We co-developed a smoking cessation app specifically tailored for women, named My Change Plan-Women (MCP-W). This app builds upon our previous gender-neutral app, MCP, by retaining its content grounded in behavioral change techniques aimed at supporting tobacco reduction and cessation. This includes quit goal setting, identifying triggers to smoking, creating coping strategies, tracking cigarettes and cravings, and assessing financial savings from quitting smoking. The MCP-W app contains additional gender-informed content that acknowledges barriers to quitting, such as coping with stress, having smokers in one's social circle, and managing unpleasant emotions. This content is delivered through testimonials and animated videos. This study is a prospective, single-group, mixed-methods investigation in which 30 women smokers will trial the app for a period of 28 days. Once participants provide informed consent, they will complete a baseline survey and download the app on their smartphones. After 28 days, participants will complete follow-up surveys. Acceptability will be assessed using the Theoretical Framework of Acceptability, which evaluates whether participants perceive the app as helpful in changing their smoking. The app will be deemed acceptable if the majority of participants rate it as such, and feasible if the majority of the participants use it for at least 7 days. Furthermore, after the 28-day trial period, participants will complete a semi-structured interview regarding their experience with the app and suggestions for improvement. RESULTS: Development of the MCP-W app was completed in September 2023. Participant recruitment for testing of the app commenced in February 2024. CONCLUSIONS: Offering smoking cessation support tailored specifically to address the unique needs of women through a smartphone app represents a novel approach. This study will test whether women who smoke perceive this approach to be acceptable and feasible in their journey towards smoking cessation.

The effects of alcohol use on smoking cessation treatment with nicotine replacement therapy: An observational study.

INTRODUCTION: Concurrent users of tobacco and alcohol are at greater risk of harm than use of either substance alone. It remains unclear how concurrent tobacco and alcohol use affects smoking cessation across levels of alcohol use and related problems. This study assessed the relationship between smoking cessation and levels of alcohol use problems. METHODS: 59,018 participants received nicotine replacement therapy through a smoking cessation program. Alcohol use and related symptoms were assessed using the Alcohol Use Disorders Identification Test (AUDIT-10) and the AUDIT-Concise (AUDIT-C). The primary outcome was 7-day point prevalence cigarette abstinence (PPA) at 6-month follow-up. We evaluated the association between alcohol use (and related problems) and smoking cessation using descriptive methods and mixed-effects logistic regression. RESULTS: 7-day PPA at 6-months was lower in groups meeting hazardous alcohol consumption criteria, with the lowest probability of smoking abstinence observed in the highest risk group. The probability of successful tobacco cessation fell with increasing levels of alcohol use and related problems. Adjusted predicted probabilities were 30.3 (95 % CI = 29.4, 31.1) for non-users, 30.2 (95 % CI = 29.4, 31.0) for low-risk users, 29.0 (95 % CI = 28.1, 29.9) for those scoring below 8 on the AUDIT-10, 27.3 (95 % CI = 26.0, 28.6) for those scoring 8-14, and 24.4 (95 % CI = 22.3, 26.5) for those scoring 15 or higher. CONCLUSION: Heavy, hazardous alcohol use is associated with lower odds of successfully quitting smoking compared to low or non-use of alcohol. Targeting alcohol treatment to this group may improve tobacco cessation outcomes.

Concurrent E-cigarette Use While Enrolled in a Smoking Cessation Program: Associations Between Frequency of Use, Motives for Use, and Smoking Cessation.

INTRODUCTION: Trial evidence suggests that e-cigarettes may aid in quitting smoking, while observational studies have found conflicting results. However, many observational studies have not adjusted for important differences between e-cigarette users and non-users. AIMS AND METHODS: We aimed to determine the association between e-cigarette use frequency and motivation to use e-cigarettes to quit smoking, and smoking cessation using data from Canada's largest smoking cessation program. Participants who completed a baseline assessment and 6-month follow-up questionnaire were divided post hoc into four groups based on their self-reported e-cigarette use during the 30 days before baseline: (1) non-users; (2) users of e-cigarettes not containing nicotine; (3) occasional users; and (4) frequent users. Occasional and frequent users were further divided into two groups based on whether they reported using e-cigarettes to quit smoking. Abstinence at 6-month follow-up (7-day point prevalence abstinence) was compared among groups. RESULTS: Adjusted quit probabilities were significantly higher (both p < .001) for frequent baseline e-cigarette users (31.6%; 95% CI = 29.3%, 33.8%) than for non-users (25.8%; 25.3% and 26.3%) or occasional users (24.2%; 22.5% and 26.0%). Unadjusted proportions favored non-users over occasional users (p < .001), but this was not significant after adjustment (p = .06). People using e-cigarettes to quit smoking were not likelier than other users to be successful, but were likelier to report frequent e-cigarette use during follow-up. CONCLUSIONS: Frequent baseline e-cigarette use predicted successful smoking cessation, compared to occasional and non-users. Use of e-cigarettes to quit did not predict smoking cessation but was associated with continued use during follow-up, perhaps due in part to planned transitions to e-cigarettes. IMPLICATIONS: Prior observational studies investigating e-cigarette use for smoking cessation have found that occasional users have poorer outcomes than either frequent or non-users. Consistent with these studies, occasional users in our data also had poorer outcomes. However, after adjustment for variables associated with cessation success, we found that cessation probabilities did not differ between occasional and non-users. These findings are consistent with trial data showing the benefit of e-cigarette use among people trying to quit smoking. Results of this study suggest that differences between trials and previous observational studies may be because of unaddressed confounding in the latter.

Healthcare providers' perspectives on implementing a brief physical activity and diet intervention within a primary care smoking cessation program: a qualitative study.

BACKGROUND: Post-smoking-cessation weight gain can be a major barrier to quitting smoking; however, adding behavior change interventions for physical activity (PA) and diet may adversely affect smoking cessation outcomes. The "Picking up the PACE (Promoting and Accelerating Change through Empowerment)" study assessed change in PA, fruit/vegetable consumption, and smoking cessation by providing a clinical decision support system for healthcare providers to utilize at the intake appointment, and found no significant change in PA, fruits/vegetable consumption, or smoking cessation. The objective of this qualitative study was to explore the factors affecting the implementation of the intervention and contextualize the quantitative results. METHODS: Twenty-five semi-structured interviews were conducted with healthcare providers, using questions based on the National Implementation Research Network's Hexagon Tool. The data were analyzed using the framework's standard analysis approach. RESULTS: Most healthcare providers reported a need to address PA and fruit/vegetable consumption in patients trying to quit smoking, and several acknowledged that the intervention was a good fit since exercise and diet could improve smoking cessation outcomes. However, many healthcare providers mentioned the need to explain the fit to the patients. Social determinants of health (e.g., low income, food insecurity) were brought up as barriers to the implementation of the intervention by a majority of healthcare providers. Most healthcare providers recognized training as a facilitator to the implementation, but time was mentioned as a barrier by many of healthcare providers. Majority of healthcare providers mentioned allied health professionals (e.g., dieticians, physiotherapists) supported the implementation of the PACE intervention. However, most healthcare providers reported a need for individualized approach and adaptation of the intervention based on the patients' needs when implementing the intervention. The COVID-19 pandemic was found to impact the implementation of the PACE intervention based on the Hexagon Tool indicators. CONCLUSION: There appears to be a need to utilize a flexible approach when addressing PA and fruit/vegetable consumption within a smoking cessation program, based on the context of clinic, the patients' it is serving, and their life circumstances. Healthcare providers need support and external resources to implement this particular intervention. NAME OF THE REGISTRY: Clinicaltrials.gov. TRIAL REGISTRATION NUMBER: NCT04223336. DATE OF REGISTRATION: 7 January 2020 Retrospectively registered. URL OF TRIAL REGISTRY RECORD: https://classic. CLINICALTRIALS: gov/ct2/show/NCT04223336 .

Cumulative Prenatal Risk Factors and Developmental Coordination Disorder in Young Children.

OBJECTIVES: To examine the effect of cumulative prenatal risk factors (RFs) on the presence of Developmental Coordination Disorder (DCD) in young children. METHODS: Participants (N = 589, 338 boys, Mage = 4.5 ± 0.5 years) were from a larger cohort study, the Coordination and Activity Tracking in Children (CATCH). Motor coordination was assessed using the Movement Assessment Battery for Children- 2nd Edition. Children were classified as at risk for DCD (DCDr) based on European Academy of Childhood Disability guidelines. RFs were obtained through a parent-completed survey. A multiple logistic regression was conducted to examine the effect of the RFs on DCD. RESULTS: Results showed that the odds of a child having DCDr are significantly higher with a greater total number of prenatal RFs, after adjustment for mother's age at child's birth, child's sex, child's age, marital status and total annual household income (OR = 1.48, p < 0.01). CONCLUSIONS: These findings warrant further investigation into the cumulative impact of multiple prenatal RFs and whether specific combinations of RFs might be more strongly linked to DCD than others. These results provide additional insight into possible causes and prevention of DCD.

Type-switching of short-acting nicotine replacement therapy products and its implications for treatment.

INTRODUCTION: The frequency with which people change from one medical treatment to another partly reflects satisfaction with the initial intervention. In this secondary analysis of observational data, we explore switch rates for short-acting nicotine replacement therapy (NRT) products, which differ in mode of use, rapidity of action, and adverse effect profiles. METHODS: We analyzed data from 46,882 participants in the Smoking Treatment for Ontario Patients program who received short-acting NRT (gum, lozenge, oral spray, or inhaler) at their initial visit. We used discrete-time survival analysis to model the time elapsing before a change was made to another product and included time-by-product interactions to explore differences over time and product-by-previous-quit-attempts interactions to examine the importance of potential previous exposure to NRT. We adjusted both models for a range of sociodemographic and tobacco use variables. RESULTS: Overall, 37.9% (95% CI = 37.5%, 38.4%) of participants switched to a different product at their first follow-up visit. Time-averaged and adjusted switch probabilities were 21.1% for gum, 23.8% for lozenge, 26.0% for inhaler, and 28.8% for oral spray. Switch rates were highest for people without past quit attempts, for women, and for people also using the nicotine patch. CONCLUSIONS: People frequently switch from one type of short-acting NRT to another. We did not assess reasons for switching, but they may include adverse effects, impracticality, and perceived ineffectiveness. Given the superior effectiveness of combination treatment, making multiple products available may improve outcomes by allowing some people to continue with short-acting products for longer.

Cannabis Use Among Adults in Cigarette Smoking Cessation Treatment in Ontario, Canada: Prevalence and Association With Tobacco Cessation Outcome, 2015-2021.

Objectives. To examine cannabis use prevalence and its association with tobacco cessation among adults enrolled in cigarette smoking cessation treatment before and after Canada legalized recreational cannabis in October 2018. Methods. The sample comprised 83 206 adults enrolled in primary care-based cigarette smoking cessation treatment between 2015 and 2021 in Ontario, Canada. Past-30-day cannabis use was self-reported at enrollment and cigarette smoking abstinence at 6-month follow-up. Results. Past-30-day prevalence of cannabis use increased from 20.2% in 2015 to 37.7% in 2021. The prevalence increased linearly both before and after legalization. Cannabis and tobacco co-use was associated with lower odds of self-reported cigarette smoking abstinence at 6-month follow-up than tobacco use only (24.4% vs 29.3%; odds ratio [OR] = 0.78; 95% confidence interval [CI] = 0.75, 0.81). This association was attenuated after adjustment for covariates (OR = 0.93; 95% CI = 0.89, 0.97) and weakened slightly over time. Conclusions. Cannabis use prevalence almost doubled from 2015 to 2021 among primary care patients in Ontario seeking treatment to quit cigarettes and was associated with poorer quit outcomes. Further research into the impact of cannabis policy on cannabis and tobacco co-use is warranted to mitigate harm. (Am J Public Health. 2024;114(1):98-107. https://doi.org/10.2105/AJPH.2023.307445).

AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study.

BACKGROUND: Varenicline is a pharmacological intervention for tobacco dependence that is safe and effective in facilitating smoking cessation. Enhanced adherence to varenicline augments the probability of prolonged smoking abstinence. However, research has shown that one-third of people who use varenicline are nonadherent by the second week. There is evidence showing that behavioral support helps with medication adherence. We have designed an artificial intelligence (AI) conversational agent or health bot, called "ChatV," based on evidence of what works as well as what varenicline is, that can provide these supports. ChatV is an evidence-based, patient- and health care provider-informed health bot to improve adherence to varenicline. ChatV has been programmed to provide medication reminders, answer questions about varenicline and smoking cessation, and track medication intake and the number of cigarettes. OBJECTIVE: This study aims to explore the feasibility of the ChatV health bot, to examine if it is used as intended, and to determine the appropriateness of proceeding with a randomized controlled trial. METHODS: We will conduct a mixed methods feasibility study where we will pilot-test ChatV with 40 participants. Participants will be provided with a standard 12-week varenicline regimen and access to ChatV. Passive data collection will include adoption measures (how often participants use the chatbot, what features they used, when did they use it, etc). In addition, participants will complete questionnaires (at 1, 4, 8, and 12 weeks) assessing self-reported smoking status and varenicline adherence, as well as questions regarding the acceptability, appropriateness, and usability of the chatbot, and participate in an interview assessing acceptability, appropriateness, fidelity, and adoption. We will use "stop, amend, and go" progression criteria for pilot studies to decide if a randomized controlled trial is a reasonable next step and what modifications are required. A health equity lens will be adopted during participant recruitment and data analysis to understand and address the differences in uptake and use of this digital health solution among diverse sociodemographic groups. The taxonomy of implementation outcomes will be used to assess feasibility, that is, acceptability, appropriateness, fidelity, adoption, and usability. In addition, medication adherence and smoking cessation will be measured to assess the preliminary treatment effect. Interview data will be analyzed using the framework analysis method. RESULTS: Participant enrollment for the study will begin in January 2024. CONCLUSIONS: By using predetermined progression criteria, the results of this preliminary study will inform the determination of whether to advance toward a larger randomized controlled trial to test the effectiveness of the health bot. Additionally, this study will explore the acceptability, appropriateness, fidelity, adoption, and usability of the health bot. These insights will be instrumental in refining the intervention and the health bot. TRIAL REGISTRATION: ClinicalTrials.gov NCT05997901; https://classic.clinicaltrials.gov/ct2/show/NCT05997901. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53556.

Primary healthcare provider experience of knowledge brokering interventions for mood management.

Background: Knowledge brokering is a knowledge translation strategy used in healthcare settings to facilitate the implementation of evidence into practice. How healthcare providers perceive and respond to various knowledge translation approaches is not well understood. This qualitative study used the Theoretical Domains Framework to examine healthcare providers' experiences with receiving one of two knowledge translation strategies: a remote knowledge broker (rKB); or monthly emails, for encouraging delivery of mood management interventions to patients enrolled in a smoking cessation program. Methods: Semi-structured interviews were conducted with 21 healthcare providers recruited from primary care teams. We used stratified purposeful sampling to recruit participants who were allocated to receive either the rKB, or a monthly email-based knowledge translation strategy as part of a cluster randomized controlled trial. Interviews were structured around domains of the Theoretical Domains Framework (TDF) to explore determinants influencing practice change. Data were coded into relevant domains. Results: Both knowledge translation strategies were considered helpful prompts to remind participants to deliver mood interventions to patients presenting depressive symptoms. Neither strategy appeared to have influenced the health care providers on the domains we probed. The domains pertaining to knowledge and professional identity were perceived as facilitators to implementation, while domains related to beliefs about consequences, emotion, and environmental context acted as barriers and/or facilitators to healthcare providers implementing mood management interventions. Conclusion: Both strategies served as reminders and reinforced providers' knowledge regarding the connection between smoking and depressed mood. The TDF can help researchers better understand the influence of specific knowledge translation strategies on healthcare provider behavior change, as well as potential barriers and facilitators to implementation of evidence-informed interventions. Environmental context should be considered to address challenges and facilitate the movement of knowledge into clinical practice.

Clinical guidance for e-cigarette (vaping) cessation: Results from a modified Delphi panel approach.

Individuals seek help to stop their use of e-cigarettes from their healthcare practitioners. However, there is a paucity of published work addressing e-cigarette cessation methods empirically, and what evidence that is available is weak. Therefore, we developed an expert informed clinical resource to guide practitioners helping their clients quit using e-cigarettes. We conducted a modified Delphi process between September and December 2021 to reach consensus on clinical recommendations for e-cigarette cessation. Expert and Peer Panel members (n = 28) voted and provided feedback on the recommendations through three rounds of structured surveys, a discussion board, and one intermediate survey. The penultimate knowledge products underwent usability testing and were finalized based on user feedback. The Expert Panel maintained a 100% response rate for rounds 1 and 2 and 96% for round 3; the Peer Panel achieved a 100% response rate for all three rounds of the modified Delphi process. Consensus was reach on 24 recommendations and 2 statements spanning eight domains: severity and dependence; general approaches; treatment approaches; dual use; pharmacotherapy strategies; behavioural therapy strategies; harm reduction; and relapse prevention. Two additional 'no agreement' statements that did not reach consensus are included in the guidance resource. The recommendations were also contextualized for the following groups: adults; youth; people who are pregnant, breastfeeding and/or chestfeeding; and people with mental illness and/or substance use issues. The recommendations listed in the resource provide general clinical guidance on e-cigarette cessation to assist healthcare practitioners in the treatment planning process.

Cocreation of a conversational agent to help patients adhere to their varenicline treatment: A study protocol.

Objective: Varenicline is the most efficacious approved smoking cessation medication, making it one of the most cost-effective clinical interventions for reducing tobacco-related morbidity and mortality. Adhering to varenicline is strongly associated with smoking cessation. Healthbots have the potential to help people adhere to their medications by scaling up evidence-based behavioral interventions. In this protocol, we outline how we will follow the UK's Medical Research Council's guidance to codesign a theory-informed, evidence-based, and patient-centered healthbot to help people adhere to varenicline. Methods: The study will utilize the Discover, Design and Build, and Test framework and will include three phases: (a) a rapid review and interviews with 20 patients and 20 healthcare providers to understand barriers and facilitators to varenicline adherence (Discover phase); (b) Wizard of Oz test to design the healthbot and get a sense of the questions that chatbot has to be able to answer (Design phase); and (c) building, training, and beta-testing the healthbot (Building and Testing phases) where the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework will be used to develop the healthbot using the simplest sensible solution, and 20 participants will beta test the healthbot. We will use the Capability, Opportunity, Motivation-Behavior (COM-B) model of behavior change and its associated framework, the Theoretical Domains Framework, to organize the findings. Conclusions: The present approach will enable us to systematically identify the most appropriate features for the healthbot based on a well-established behavioral theory, the latest scientific evidence, and end users' and healthcare providers' knowledge.

Coordinating smoking cessation treatment with menstrual cycle phase to improve quit outcomes (MC-NRT): study protocol for a randomized controlled trial.

BACKGROUND: Women experience greater difficulty achieving smoking abstinence compared to men. Recent evidence suggests that hormonal fluctuations during different phases of the menstrual cycle can contribute to lower smoking abstinence rates following a quit attempt among women. However, these findings are limited by small sample sizes and variability among targeted smoking quit dates. This clinical trial aims to clarify whether targeting the quit date to the follicular or luteal phase of the menstrual cycle can improve smoking abstinence. METHODS: Participants will enroll in an online smoking cessation program providing nicotine replacement therapy (NRT) and behavioral support. We will randomize 1200 eligible individuals to set a target quit date: (1) during the mid-luteal phase, (2) during the mid-follicular phase, or (3) 15-30 days after enrollment with no regard to the menstrual cycle phase (usual practice). Participants will receive a 6-week supply of combination NRT consisting of a nicotine patch plus their choice of nicotine gum or lozenge. Participants will be instructed to start using NRT on their target quit date. Optional behavioral support will consist of a free downloadable app and brief videos focusing on building a quit plan, coping with cravings, and relapse prevention, delivered via e-mail. Smoking status will be assessed via dried blood spot analysis of cotinine concentration at 7 days, 6 weeks, and 6 months post-target quit date. DISCUSSION: We aim to overcome the limitations of previous studies by recruiting a large sample of participants and assigning target quit dates to the middle of both the follicular and luteal phases. The results of the trial can further elucidate the effects of the menstrual cycle on smoking cessation outcomes and whether it is beneficial to combine menstrual cycle phase timing strategies with accessible and low-cost NRT. TRIAL REGISTRATION: ClinicalTrials.gov NCT05515354. Registered on August 23, 2022.

Adequacy of nicotine replacement and success quitting tobacco in clinical populations: An observational study.

BACKGROUND: Nicotine replacement therapy (NRT) for smoking cessation is an effective intervention that reduces urges to smoke by substituting a safer source of nicotine. NRT dosing is imprecise, however, and there is some evidence that patients and providers are reluctant to use the larger doses that may be appropriate for some people who smoke. In this analysis, we assess the relationship between cigarettes smoked and NRT prescribed, and between adequacy of nicotine replacement and cessation success. METHODS: We analyzed data from 84,667 patients and 492 clinics participating in a province-wide NRT-based smoking cessation program. We evaluated the association between cigarettes per day (CPD) and NRT dose using descriptive methods, and used mixed-effects logistic regression to identify associations between dose and outcome. We used fractional polynomials to fit non-linear associations and multiple imputation to address missing data. RESULTS: Prescribed NRT doses increased much less than proportionately with CPD, with a median for higher CPD levels of about 1 mg/CPD at baseline. Doses did not increase at subsequent visits for people who continued to smoke daily. Dose-response curves derived from our model showed that initial doses below about 2 mg/CPD/day were associated with poorer outcomes. CONCLUSIONS: Under-dosing of NRT, both at treatment initiation and subsequent clinical contacts, is likely to contribute to the poorer treatment outcomes seen among people who smoke heavily. Improved communication with providers and patients is probably needed to overcome reluctance to use larger doses.

Outcomes Among People With Schizophrenia Participating in General-Population Smoking Cessation Treatment: An Observational Study.

OBJECTIVE: People with schizophrenia are much more likely than others to smoke tobacco, raising risks of disease and premature mortality. These individuals are also less likely to quit successfully after treatment, but the few existing clinical and observational studies have been limited by small sample sizes, and have generally considered specialized treatment approaches. In this analysis, we examine outcomes, service use, and potential explanatory variables in a large sample of people with schizophrenia treated in a general-population cessation program. METHOD: Our sample comprised 3,011 people with schizophrenia and 77,790 controls receiving free nicotine replacement therapy through 400 clinics and health centres. We analysed self-reported 7-day abstinence or reduction at 6-month follow-up, as well as the number of visits attended and self-reported difficulties in quitting. We adjusted for demographic, socioeconomic, and health variables, and used multiple imputation to address missing data. RESULTS: Abstinence was achieved by 16.2% (95% confidence interval [CI], 14.5% to 17.8%) of people with schizophrenia and 26.4% (95% CI, 26.0% to 26.7%) of others (absolute difference = 10.2%; 95% CI, 8.5% to 11.9%; P < 0.001). After adjustment, this difference was reduced to 7.3% (95% CI, 5.4% to 9.3%; P < 0.001). Reduction in use was reported by 11.8% (95% CI, 10.3% to 13.3%) and 12.5% (95% CI, 12.2% to 12.8%), respectively; this difference was nonsignificant after adjustment. People with schizophrenia attended more clinic visits (incidence rate ratio [IRR] = 1.15, 95% CI = 1.12% to 1.18%, P < 0.001) and reported more difficulties related to "being around other smokers" (odds ratio [OR] = 1.28; 95% CI, 1.11% to 1.47%; P = 0.001). CONCLUSION: There is abundant demand for tobacco cessation treatment in this population. Outcomes were substantially poorer for people with schizophrenia, and this difference was not explained by covariates. Cessation remained much better than for unaided quit attempts, however, and engagement was high, demonstrating that people with schizophrenia benefit from nonspecialized pharmacological treatment programs.

Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study.

BACKGROUND: The COVID-19 pandemic disrupted the delivery of diabetes care and worsened mental health among many patients with type 2 diabetes (T2D). This disruption puts patients with T2D at risk for poor diabetes outcomes, especially those who experience social disadvantage due to socioeconomic class, rurality, or ethnicity. The appropriate use of communication technology could reduce these gaps in diabetes care created by the pandemic and also provide support for psychological distress. OBJECTIVE: The purpose of this study is to test the feasibility of an innovative co-designed Technology-Enabled Collaborative Care (TECC) model for diabetes management and mental health support among adults with T2D. METHODS: We will recruit 30 adults with T2D residing in Ontario, Canada, to participate in our sequential explanatory mixed methods study. They will participate in 8 weekly web-based health coaching sessions with a registered nurse, who is a certified diabetes educator, who will be supported by a digital care team (ie, a peer mentor, an addictions specialist, a dietitian, a psychiatrist, and a psychotherapist). Assessments will be completed at baseline, 4 weeks, and 8 weeks, with a 12-week follow-up. Our primary outcome is the feasibility and acceptability of the intervention, as evident by the participant recruitment and retention rates. Key secondary outcomes include assessment completion and delivery of the intervention. Exploratory outcomes consist of changes in mental health, substance use, and physical health behaviors. Stakeholder experience and satisfaction will be explored through a qualitative descriptive study using one-on-one interviews. RESULTS: This paper describes the protocol of the study. The recruitment commenced in June 2021. This study was registered on October 29, 2020, on ClinicalTrials.gov (Registry ID: NCT04607915). As of June 2022, all participants have been recruited. It is anticipated that data analysis will be complete by the end of 2022, with study findings available by the end of 2023. CONCLUSIONS: The development of an innovative, technology-enabled model will provide necessary support for individuals living with T2D and mental health challenges. This TECC program will determine the feasibility of TECC for patients with T2D and mental health issues. TRIAL REGISTRATION: ClinicalTrials.gov NCT04607915; https://clinicaltrials.gov/ct2/show/NCT04607915. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39724.

Effectiveness of Bupropion and Varenicline for Smokers With Baseline Depressive Symptoms.

INTRODUCTION: Smokers with concurrent depression are less likely to achieve abstinence, even with pharmacotherapy. The purpose of this secondary data analysis was to evaluate if the presence of any depressive symptoms at baseline alters the effectiveness of bupropion and varenicline for smoking cessation. AIMS AND METHODS: Eligible participants were enrolled via the internet and randomized 1:1 to receive a 12-week supply of either bupropion (n = 465) or varenicline (n = 499). Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-2). Follow-up surveys were conducted at weeks 4, 8, 12, 26, and 52 to assess self-reported quit. The primary outcome was 7-day point prevalence abstinence at 12 weeks follow-up (end-of-treatment). RESULTS: Participants who endorsed any depressive symptoms (PHQ-2 > 0; n = 280) were less likely to be quit at end-of-treatment compared to participants who did not endorse any symptoms (PHQ-2 = 0; n = 684) (OR = 0.56, 95% CI: 0.38 to 0.8, p = .003). Within the varenicline group, quit outcomes did not differ between those with and without depressive symptoms (21.3% vs. 26.9%, respectively). Within the bupropion group, however, those with symptoms had a significantly reduced quit rate compared to those without symptoms (7.0% vs. 17.3%, respectively). CONCLUSIONS: The presence of even one symptom of depression at the start of a quit attempt may adversely affect quit outcomes. Patients should be assessed for depressive symptoms when planning to quit smoking as it may inform the approach to treatment. However, future studies are needed to confirm these findings. IMPLICATIONS: Findings from the current study illustrate the importance of evaluating baseline sub-clinical depressive symptoms before a quit attempt using first-line pharmacotherapies. This secondary analysis of a large-scale randomized trial suggests that bupropion may be less effective for those with baseline depressive symptoms while varenicline may be equally effective for those with and without depressive symptoms.

Stability of M-ABC-2-measured motor functioning in early childhood: Evidence from a large prospective cohort.

AIM: Some studies have reported that children with poor motor functioning tend to improve over time. However, much existing research does not account for regression towards the mean (RTM). Here, we examine measurement stability among 589 children aged 4-5 years. METHOD: We administered the Movement Assessment Battery for Children 2nd Edition annually to 269 children initially scoring above and 252 at or below the 16th percentile. We measured agreement between year 1 and year 2 standard scores using Pearson correlation and derived expected regression towards the mean (RTM). We then regressed follow-up on baseline scores, controlling for relative age, sex, and exact interval between assessments. Finally, we performed a small illustrative simulation. OUTCOMES AND RESULTS: The mean score in the poor-coordination group rose from 5.6 (SD = 1.5) to 7.2 (SD = 2.8). Year 1 and year 2 scores were correlated at r = 0.66, corresponding to predicted RTM in the MI group of 1.56, close to the observed change of 1.57. Degree of change was not associated with time between assessments. INTERPRETATION: Observed improvements in motor functioning were consistent with measurement error. The stability of motor functioning may be greater than it appears from past research, and reported functional improvements in some studies may be illusory. WHAT THIS PAPER ADDS?

The Impact of a Clinical Decision Support System for Addressing Physical Activity and Healthy Eating During Smoking Cessation Treatment: Hybrid Type I Randomized Controlled Trial.

BACKGROUND: People who smoke have other risk factors for chronic diseases, such as low levels of physical activity and poor diet. Clinical decision support systems (CDSSs) might help health care practitioners integrate interventions for diet and physical activity into their smoking cessation programming but could worsen quit rates. OBJECTIVE: The aims of this study are to assess the effects of the addition of a CDSS for physical activity and diet on smoking cessation outcomes and to assess the implementation of the study. METHODS: We conducted a pragmatic hybrid type I effectiveness-implementation trial with 232 team-based primary care practices in Ontario, Canada, from November 2019 to May 2021. We used a 2-arm randomized controlled trial comparing a CDSS addressing physical activity and diet to treatment as usual and used the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to measure implementation outcomes. The primary outcome was self-reported 7-day tobacco abstinence at 6 months. RESULTS: We enrolled 5331 participants in the study. Of these, 2732 (51.2%) were randomized to the intervention group and 2599 (48.8%) to the control group. At the 6-month follow-up, 29.7% (634/2137) of respondents in the intervention arm and 27.3% (552/2020) in the control arm reported abstinence from tobacco. After multiple imputation, the absolute group difference was 2.1% (95% CI -0.5 to 4.6; F1,1000.42=2.43; P=.12). Mean exercise minutes changed from 32 (SD 44.7) to 110 (SD 196.1) in the intervention arm and from 32 (SD 45.1) to 113 (SD 195.1) in the control arm (group effect: B=-3.7 minutes; 95% CI -17.8 to 10.4; P=.61). Servings of fruit and vegetables changed from 2.64 servings to 2.42 servings in the intervention group and from 2.52 servings to 2.45 servings in the control group (incidence rate ratio for intervention group=0.98; 95% CI 0.93-1.02; P=.35). CONCLUSIONS: A CDSS for physical activity and diet may be added to a smoking cessation program without affecting the outcomes. Further research is needed to improve the impact of integrated health promotion interventions in primary care smoking cessation programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04223336 https://www.clinicaltrials.gov/ct2/show/NCT04223336. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19157.

Can nicotine replacement therapy be personalized? A statistical learning analysis.

BACKGROUND: Technology has made automated care personalization practical, but useful personalization requires information about systematic differences between individuals in the effectiveness of different interventions. Here, we used observational data to search for differences in smoking cessation treatment outcomes associated with interactions between participant characteristics and different types and doses of nicotine replacement therapy (NRT). METHODS: We analyzed 33,077 enrollments in a large primary care smoking cessation program in Ontario, Canada. We considered 10 types and combinations of NRT, as well as the provided daily dose of nicotine. We used ridge regression to fit one main effects model and one model including all possible interactions between these measures and a range of demographic and health variables. We then compared the predictive accuracy of these models in a held-out 25 % testing subset using areas under the receiver operating characteristic curve (AUROC) and the integrated discrimination improvement index (IDI). We used random forest multiple imputation to address missing data. RESULTS: The model including main effects only modestly predicted quit success at 6 months (AUROC = 0.646, 95 % CI = 0.631, 0.660). The final model with all interactions had essentially identical performance (AUROC = 0.640, 95 % CI = 0.626, 0.654; IDI = -0.0066). CONCLUSION: We found no evidence of meaningful interactions between treatment outcomes and participants' characteristics, NRT type, or NRT dose. Although data are observational, these findings suggest that the effectiveness of different types and doses of NRT do not vary substantially with participant characteristics. Personalization based on the overall likelihood of quit success, or using genetic or other biological data, remains possible.