A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.

Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use. Our method consists of a longitudinal submodel-a dynamic factor model-that models changes in the time-varying latent states and a cumulative risk submodel-a Poisson regression model-that connects the latent states with the total number of events. In the motivating data, both the predictors-the underlying psychological states-and the event outcome-the number of cigarettes smoked-are partially unobservable; we account for this incomplete information in our proposed model and estimation method. We take a two-stage approach to estimation that leverages existing software and uses importance sampling-based weights to reduce potential bias. We demonstrate that these weights are effective at reducing bias in the cumulative risk submodel parameters via simulation. We apply our method to a subset of data from a smoking cessation study to assess the association between psychological state and cigarette smoking. The analysis shows that above-average intensities of negative mood are associated with increased cigarette use.

Examining Links Between Distinct Affective States and Tobacco Lapse During a Cessation Attempt Among African Americans: A Cohort Study.

BACKGROUND: Affect states are posited to play a pivotal role in addiction-related processes, including tobacco lapse (i.e., smoking during a quit attempt), and distinct affective states (e.g., joy vs. happiness) may differentially influence lapse likelihood. However, few studies have examined the influence of distinct affective states on tobacco lapse. PURPOSE: This study examines the influence of 23 distinct affect states on tobacco lapse among a sample of tobacco users attempting to quit. METHODS: Participants were 220 adults who identified as African American (50% female, ages 18-74). Ecological momentary assessment was used to assess affect and lapse in real-time. Between and within-person associations testing links between distinct affect states and lapse were examined with multilevel modeling for binary outcomes. RESULTS: After adjusting for previous time's lapse and for all other positive or negative affect items, results suggested that at the between-person level, joy was associated with lower odds of lapse, and at the within-person level, attentiveness was associated with lower odds of lapse. Results also suggested that at the between-person level, guilt and nervous were associated with higher odds of lapse, and at the within-person level, shame was associated with higher odds of lapse. CONCLUSIONS: The present study uses real-time, real-world data to demonstrate the role of distinct positive and negative affects on momentary tobacco lapse. This work helps elucidate specific affective experiences that facilitate or hinder the ability to abstain from tobacco use during a quit attempt.

SCALE-UP II: protocol for a pragmatic randomised trial examining population health management interventions to increase the uptake of at-home COVID-19 testing in community health centres.

INTRODUCTION: SCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs). METHODS AND ANALYSIS: The trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2×2×2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2×2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors. ETHICS AND DISSEMINATION: The protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: NCT05533918 and NCT05533359.

Co-action and changes in alcohol use during a smoking cessation attempt.

AIMS: Three smoking cessation studies (CARE, Break Free, Por Nuestra Salud [PNS]) were used to measure changes in average alcohol consumption, binge drinking and alcohol-related problems during a smoking cessation attempt and to explore co-action with smoking abstinence. DESIGN: CARE and PNS were longitudinal cohort cessation studies; Break Free was a two-arm randomized clinical trial. SETTING: Texas, USA. PARTICIPANTS: Participants were current smokers who were recruited from the community and received smoking cessation interventions. All participants received nicotine replacement therapy and smoking cessation counseling. CARE included 424 smokers (1/3 White, 1/3 African American and 1/3 Latino); Break Free included 399 African American smokers; PNS included 199 Spanish-speaking Mexican-American smokers. MEASUREMENTS: Weekly alcohol consumption was collected multiple times pre and post-quit, and binge drinking and alcohol-related problems were collected at baseline and 26 weeks post-quit. Analyses included only those who indicated current alcohol use. FINDINGS: Average alcohol consumption decreased from baseline to 26 weeks post-quit in CARE (F = 17.09, P < 0.001), Break Free (F = 12.08, P < 0.001) and PNS (F = 10.21, P < 0.001). Binge drinking decreased from baseline to 26 weeks post-quit in CARE (F = 3.94, P = 0.04) and Break Free (F = 10.41, P < 0.001) but not PNS. Alcohol-related problems decreased from baseline to 26 weeks post-quit in CARE (Chi-sq = 6.41, P = 0.010) and Break Free (Chi sq = 14.44, P = 0.001), but not PNS. CONCLUSIONS: Among current drinkers, alcohol use/problems appear to decrease during a smoking cessation attempt and remain low through 26 weeks after the quit attempt. Little evidence was found for co-action, with smoking abstainers and relapsers showing similar change in alcohol use/problems.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Increasing the reach of evidence-based interventions for weight management and diabetes prevention among Medicaid patients: study protocol for a pilot Sequential Multiple Assignment Randomised Trial.

INTRODUCTION: Over 40% of US adults meet criteria for obesity, a major risk factor for chronic disease. Obesity disproportionately impacts populations that have been historically marginalised (eg, low socioeconomic status, rural, some racial/ethnic minority groups). Evidence-based interventions (EBIs) for weight management exist but reach less than 3% of eligible individuals. The aims of this pilot randomised controlled trial are to evaluate feasibility and acceptability of dissemination strategies designed to increase reach of EBIs for weight management. METHODS AND ANALYSIS: This study is a two-phase, Sequential Multiple Assignment Randomized Trial, conducted with 200 Medicaid patients. In phase 1, patients will be individually randomised to single text message (TM1) or multiple text messages (TM+). Phase 2 is based on treatment response. Patients who enrol in the EBI within 12 weeks of exposure to phase 1 (ie, responders) receive no further interventions. Patients in TM1 who do not enrol in the EBI within 12 weeks of exposure (ie, TM1 non-responders) will be randomised to either TM1-Continued (ie, no further TM) or TM1 & MAPS (ie, no further TM, up to 2 Motivation And Problem Solving (MAPS) navigation calls) over the next 12 weeks. Patients in TM+ who do not enrol in the EBI (ie, TM+ non-responders) will be randomised to either TM+Continued (ie, monthly text messages) or TM+ & MAPS (ie, monthly text messages, plus up to 2 MAPS calls) over the next 12 weeks. Descriptive statistics will be used to characterise feasibility (eg, proportion of patients eligible, contacted and enrolled in the trial) and acceptability (eg, participant opt-out, participant engagement with dissemination strategies, EBI reach (ie, the proportion of participants who enrol in EBI), adherence, effectiveness). ETHICS AND DISSEMINATION: Study protocol was approved by the University of Utah Institutional Review Board (#00139694). Results will be disseminated through study partners and peer-reviewed publications. TRIAL REGISTRATION NUMBER: clinicaltrials.gov; NCT05666323.

The Effects of Scheduled Smoking Reduction and Precessation Nicotine Replacement Therapy on Smoking Cessation: Randomized Controlled Trial With Compliance.

BACKGROUND: Smoking remains a major public health problem, and it is important to provide a variety of efficacious and appealing options to encourage smokers to quit smoking. Scheduled smoking is a method of gradual reduction, preparing smokers to quit by systematically reducing cigarette consumption according to a predetermined schedule that increases the time between cigarette consumption. Gradual reduction may be preferred to abrupt quitting, but the efficacy of this cessation approach is unclear. OBJECTIVE: This study aims, first, to evaluate the overall effectiveness of scheduled smoking alone, or in combination with precessation nicotine replacement therapy (NRT), versus standard NRT starting on the quit date with no prior smoking reduction and, second, to evaluate the impact of schedule compliance on the effectiveness of the intervention. METHODS: A total of 916 participants recruited from the Houston metropolitan area were randomly assigned to 1 of the following 3 groups: scheduled smoking plus a precessation nicotine patch (n=306, 33.4%), scheduled smoking only with no precessation patch (n=309, 33.7%), and enhanced usual care (n=301, 32.9%) control. The primary abstinence outcomes were carbon monoxide-verified, self-reported, 7-day point prevalence abstinence at 2 and 4 weeks after the quit date. Unadjusted and adjusted logistic regression analyses were performed to evaluate the intervention effect. Scheduled smoking was implemented using a handheld device for 3 weeks before quitting. This trial was not registered because data collection began before July 1, 2005. RESULTS: Results for the first aim showed no overall differences in abstinence among the 3 groups in both the unadjusted and adjusted models. However, the results for the second aim showed a clear effect on abstinence by schedule compliance at 2 and 4 weeks and 6 months after quitting (odds ratio [OR] 2.01, 95% CI 1.31-3.07), 4 weeks (OR 1.58, 95% CI 1.05-2.38), and 6 months (OR 1.68, 95% CI 1.04-2.64), with the differences at 2 and 4 weeks after quitting being the most robust. We also found that scheduled smoking was related to a reduction in nicotine withdrawal, negative affect, and craving when compared with the controls. CONCLUSIONS: Scheduled smoking, when combined with precessation use of NRT, can result in significantly higher abstinence rates than usual care (abrupt quitting with NRT), particularly in the early postquit phase (2 and 4 weeks after cessation) when smokers are compliant with the procedure. Scheduled smoking also produced a better overall quitting experience by reducing symptoms of nicotine withdrawal and craving, in comparison with usual care, which could encourage future quit attempts. Studies in this area should focus on the use of counseling or other methods to improve adherence.

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies.

Objective: Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies. Method: Data from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response. Results: Although prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time. Conclusion: Our study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BACKGROUND: The Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources. METHODS: DEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes. RESULTS: In the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8). CONCLUSION: Most C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

Rapid-cycle designs to adapt interventions for COVID-19 in safety-net healthcare systems.

Racial/ethnic minority, low socioeconomic status, and rural populations are disproportionately affected by COVID-19. Developing and evaluating interventions to address COVID-19 testing and vaccination among these populations are crucial to improving health inequities. The purpose of this paper is to describe the application of a rapid-cycle design and adaptation process from an ongoing trial to address COVID-19 among safety-net healthcare system patients. The rapid-cycle design and adaptation process included: (a) assessing context and determining relevant models/frameworks; (b) determining core and modifiable components of interventions; and (c) conducting iterative adaptations using Plan-Do-Study-Act (PDSA) cycles. PDSA cycles included: Plan. Gather information from potential adopters/implementers (e.g., Community Health Center [CHC] staff/patients) and design initial interventions; Do. Implement interventions in single CHC or patient cohort; Study. Examine process, outcome, and context data (e.g., infection rates); and, Act. If necessary, refine interventions based on process and outcome data, then disseminate interventions to other CHCs and patient cohorts. Seven CHC systems with 26 clinics participated in the trial. Rapid-cycle, PDSA-based adaptations were made to adapt to evolving COVID-19-related needs. Near real-time data used for adaptation included data on infection hot spots, CHC capacity, stakeholder priorities, local/national policies, and testing/vaccine availability. Adaptations included those to study design, intervention content, and intervention cohorts. Decision-making included multiple stakeholders (e.g., State Department of Health, Primary Care Association, CHCs, patients, researchers). Rapid-cycle designs may improve the relevance and timeliness of interventions for CHCs and other settings that provide care to populations experiencing health inequities, and for rapidly evolving healthcare challenges such as COVID-19.

Racial/ethnic minority, low socioeconomic status, and rural populations experience a disproportionate burden of COVID-19. Finding ways to address COVID-19 among these populations is crucial to improving health inequities. The purpose of this paper is to describe the rapid-cycle design process for a research project to address COVID-19 testing and vaccination among safety-net healthcare system patients. The project used real-time information on changes in COVID-19 policy (e.g., vaccination authorization), local case rates, and the capacity of safety-net healthcare systems to iteratively change interventions to ensure interventions were relevant and timely for patients. Key changes that were made to interventions included a change to the study design to include vaccination as a focus of the interventions after the vaccine was authorized; change in intervention content according to the capacity of local Community Health Centers to provide testing to patients; and changes to intervention cohorts such that priority groups of patients were selected for intervention based on characteristics including age, residency in an infection "hot spot," or race/ethnicity. Iteratively improving interventions based on real-time data collection may increase intervention relevance and timeliness, and rapid-cycle adaptions can be successfully implemented in resource constrained settings like safety-net healthcare systems.

Socio-economic status moderates within-person associations of risk factors and smoking lapse in daily life.

BACKGROUND AND AIMS: Individuals of lower socio-economic status (SES) display a higher prevalence of smoking and have more diffxiculty quitting than higher SES groups. The current study investigates whether the within-person associations of key risk (e.g. stress) and protective (self-efficacy) factors with smoking lapse varies by facets of SES. DESIGN AND SETTING: Observational study using ecological momentary assessment to collect data for a 28-day period following a smoking quit attempt. Multi-level mixed models (i.e. generalized linear mixed models) examined cross-level interactions between lapse risk and protective factors and indicators of SES on smoking lapse. PARTICIPANTS: A diverse sample of 330 adult US smokers who completed a larger study examining the effects of race/ethnicity and social/environmental influences on smoking cessation. MEASUREMENTS: Risk factors included momentary urge, negative affect, stress; protective factors included positive affect, motivation, abstinence self-efficacy; SES measures: baseline measures of income and financial strain; the primary outcome was self-reported lapse. FINDINGS: Participants provided 43 297 post-quit observations. Mixed models suggested that income and financial strain moderated the effect of some risk factors on smoking lapse. The within-person association of negative [odds ratio (OR) = 0.967, 95% CI= 0.945, 0.990, P < 0.01] and positive affect (OR = 1.023, 95% CI = 1.003, 1.044, P < 0.05) and abstinence self-efficacy (OR = 1.020, 95% CI = 1.003, 1.038, P < 0.05) on lapse varied with financial strain. The within-person association of negative affect (OR = 1.005, 95% CI = 1.002, 1.008, P < 0.01), motivation (OR = 0.995, 95% CI = 0.991, 0.999, P < 0.05) and abstinence self-efficacy (OR = 0.996, 95% CI = 0.993, 0.999, P < 0.01) on lapse varied by income. The positive association of negative affect with lapse was stronger among individuals with higher income and lower financial strain. The negative association between positive affect and abstinence self-efficacy with lapse was stronger among individuals with lower financial strain, and the negative association between motivation and abstinence self-efficacy with lapse was stronger among those with higher income. The data were insensitive to detect statistically significant moderating effects of income and financial strain on the association of urge or stress with lapse. CONCLUSION: Some risk factors (e.g. momentary negative affect) exert a weaker influence on smoking lapse among lower compared to higher socio-economic status groups.

Summary of the first Brazilian Symposium on Human Biometeorology.

This brief background highlights Brazil as a 'climate-health hotspot', i.e. a country where climate affects local populations negatively through multiple pathways (Di Napoli et al. BMC Public Health 22(1):1-8, 2022). Knowledge gaps still need to be filled concerning the various climaterelated dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers in the country (Krüger et al. Int J Biometeorol 66(7):1297-1315, 2022). Motivated by this, the first Brazilian Symposium on Human Biometeorology (Simpósio Brasileiro de Biometeorologia Humana 2022) was organized and held at the Federal University of Rio Grande do Norte (UFRN) in Natal, northeastern Brazil, between July 4 and 8, 2022. The symposium was organized as a hybrid event by a committee composed of researchers acting in different regions of the country, and who had an ongoing research collaboration on matters related to human biometeorology. The event was partly sponsored by the ISB and partly self-supported by the organizers and institutions involved. The symposium aimed to promote the development of the research area on human biometeorology in Brazil in facing challenges imposed by a globally and locally changing climate. To achieve this, the symposium focused on five main topics of discussion: a) climate-driven diseases; b) thermal comfort, urban and architectural biometeorology; c) atmospheric pollution and health; d) climate change; e) climate, health and climate change. This summary highlights the main findings, future research directions, and policy implications in each topic from the presentations and panel discussions.

Thermal perception and lung function: a panel study in young adults with exercise under high outdoor temperature.

It has been observed that high temperature exposure is associated with a reduction in lung function and some possible biological mechanisms have been suggested. However, it is unclear if thermal perception plays a role in the association. From September 3rd to 15th, 2018, in Guangzhou, China, we repeatedly measured daily thermal perception and lung function among 126 participants with outdoor military training. We performed a linear mixed model and stratified analyses by the origin of students, gender, and the training period to evaluate the effects of thermal perception on lung function. A total of 399 measurements were collected. Per vote increase in thermal sensation vote towards the "hot" direction was associated with a - 0.04 L (95% CI: - 0.08 to - 0.01) decrease in forced vital capacity (FVC), and - 0.04 L (95% CI: - 0.08 to - 0.01) decrease in forced expiratory volume in 1 s (FEV1). Per grade increase towards the "very uncomfortable" direction for thermal comfort vote was associated with an increased percentage of forced expiratory volume in 1 s (FEV1%) by 1.52% (95% CI: 0.18 to 2.86). For thermal preference, with preferred cooler vote increased by one level, FVC and FEV1 decreased by - 0.05 L/s (95% CI: - 0.08 to - 0.02) and - 0.05L/s (95% CI: - 0.08 to - 0.02), respectively. The effects of thermal perception on lung function were stronger among non-local and in the first week of training. Our study suggests that in the same high-temperature environment, thermal perception is associated with lung function, even in healthy adults.

An ecological momentary assessment study of outcome expectancies and smoking lapse in daily life.

OBJECTIVE: Outcome expectancies have been identified as key components of behavior change. Expectancies related to affect control are hypothesized to play an important role in smoking cessation, such that smokers may be more likely to lapse if they believe they can control their affect by smoking and less likely if they believe they can control their affect by means other than smoking. However, little is known about whether real-time, real-world changes in affect control expectancies influence smoking lapse during a quit attempt. METHODS: A diverse sample (N = 369) of adult smokers completed ecological momentary assessment of smoking expectancies and lapse for 28 days following a quit attempt. Multilevel logistic regression was used to examine whether the difference score of positive smoking outcome expectancies (the belief that smoking would improve mood) minus positive coping outcome expectancies (the belief that something other than smoking would improve mood) was related to smoking lapse in daily life. RESULTS: There was a significant within-person association between the expectancies difference score and lapse likelihood. When the difference score was 1 unit above a person's typical level, odds of lapse increased by 18.65 % (ß = 0.174, SE = 0.024, p < .0001, OR = 1.189, 95 % CI [1.135, 1.247]). CONCLUSION: Smokers undergoing a quit attempt were more likely to lapse in moments when the difference in the belief that smoking would improve their mood minus the belief that something other than smoking would improve their mood was larger. This work has relevance for tailoring interventions to both cultivate positive coping outcome expectancies and reduce smoking outcome expectancies, and informs theoretical models about the dynamic nature of outcome expectancies.

mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.

Increasing treatment enrollment among smokers who are not motivated to quit: a randomized clinical trial.

There is a significant research-to-practice gap with respect to reaching underserved populations with evidence-based tobacco cessation treatments. Increasing enrollment in evidence-based treatments is necessary to reduce tobacco use and tobacco-related health inequities. The purpose of the current study was to evaluate whether Motivation And Problem Solving (MAPS), a flexible, holistic counseling/navigation approach delivered via phone, and proactive provision of Nicotine Replacement Therapy (NRT) would improve Quitline enrollment among a sample of low SES smokers who were not motivated to quit. In a 3×2 factorial design, cigarette smokers (N = 603) were randomized to one of six treatment conditions (Standard Treatment, MAPS-6, or MAPS-12 by NRT or no NRT). Results indicated that both MAPS-6 and MAPS-12 increased Quitline enrollment compared to Standard Treatment (ps < .03). There were no differences between MAPS conditions. NRT did not increase Quitline enrollment. MAPS is an effective intervention with the potential to be disseminated and implemented in healthcare and community settings to increase the reach of evidence-based interventions for tobacco cessation.

Trajectories of motivation and self-efficacy during a smoking quit attempt: An ecological momentary assessment study.

OBJECTIVE: Conceptual models of addiction highlight self-efficacy and motivation as key variables important during a smoking quit attempt; however, recent research has primarily focused on self-efficacy. Given the importance of motivation in these models, a clearer understanding of the effects of motivation during a smoking quit attempt is needed. METHOD: This study utilized ecological momentary assessment (EMA) to investigate the dynamic effects of motivation and self-efficacy early in a quit attempt. Participants were 356 smokers (45% male; 34% African American, 33% non-Hispanic White, 33% Latino). Participants completed EMAs of motivation, self-efficacy, and smoking for 4 days prequit through 1 week postquit, and returned for a follow-up assessment at 4 weeks postquit. Trajectory parameters of motivation and self-efficacy (mean, slope, and volatility) were analyzed in separate and combined regression models to predict smoking outcomes. RESULTS: Prequit results showed that parameters of motivation and self-efficacy were not associated with smoking on quit day. Postquit analyses revealed that participants with lower mean levels of motivation and self-efficacy were more likely to smoke at the end of Week 1. Moreover, individuals with decreasing levels of motivation over the first week of their quit attempt were more likely to be smoking at the end of Week 4. CONCLUSIONS: These findings highlight the need to incorporate dynamic measures of motivation in smoking research. Furthermore, the results underscore the value of utilizing EMA methods and trajectory parameters to gain a more nuanced understanding of the dynamic effects that key mechanisms have on smoking during a quit attempt. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Workflow analysis for design of an electronic health record-based tobacco cessation intervention in community health centers.

OBJECTIVE: Tobacco use is the leading cause of preventable morbidity and mortality in the United States. Quitlines are effective telephone-based tobacco cessation services but are underutilized. The goal of this project was to describe current clinical workflows for Quitline referral and design an optimal electronic health record (EHR)-based workflow for Ask-Advice-Connect (AAC), an evidence-based intervention to increase Quitline referrals. MATERIALS AND METHODS: Ten Community Health Center systems (CHC), which use three different EHRs, participated in this study. Methods included: 9 group discussions with CHC leaders; 33 observations/interviews of clinical teams' workflow; surveys with 57 clinical staff; and assessment of the EHR ecosystem in each CHC. Data across these methods were integrated and coded according to the Fit between Individual, Task, Technology and Environment (FITTE) framework. The current and optimal workflow were notated using Business Process Modelling Notation. We compared the requirements of the optimal workflow with EHR capabilities. RESULTS: Current workflows are inefficient in data collection, variable in who, how, and when tobacco cessation advice and referral are enacted, and lack communication between referring clinics and the Quitline. In the optimal workflow, medical assistants deliver a standardized AAC intervention during the visit intake. Referrals are submitted electronically, and there is bidirectional communication between the clinic and Quitline. We implemented AAC within all three EHRs; however, deviations from the optimal workflow were necessary. CONCLUSION: Current workflows for Quitline referral are inefficient and ineffective. We propose an optimal workflow and discuss improvements in EHR capabilities that would improve the implementation of AAC.

Effects of tree plantings and aspect ratios on pedestrian visual and thermal comfort using scaled outdoor experiments.

Urban trees ameliorate heat stress for urban dwellers. However, it is difficult to quantitatively assess the integrated impacts of tree planting and street layouts on visual and thermal comfort in simulations and urban field experiments. We conducted scaled outdoor experiments in Guangzhou to investigate the influence of tree plantings on pedestrian visual and thermal comfort in street canyons with various aspect ratios (H/W = 1, 2, 3; H = 1.2 m). We considered the effects of tree crown covers (big and small crown) and tree planting densities (ρ = 1, 0.5) on pedestrian illuminance level and two thermal comfort indices (Physiological Equivalent Temperature: PET and Index of Thermal Stress: ITS). When ρ = 1, trees in most cases reduce pedestrian illuminance (maximum 140.0klux) and improve visual comfort. Decreasing ρ from 1 to 0.5 increases the illuminance (maximum 179.5klux) in the streets with big crown trees (H/W = 1, 2) and in the street with small crown trees (H/W = 2). When ρ = 1 (H/W = 1, 2), big crown trees decrease the peak daytime PET (by about 4.0 °C) and ITS (by about 285 W). Small crown trees (ρ = 1, H/W = 1, 2) produce a warming effect on peak daytime PET (2.0-3.0 °C), but a reduction in ITS is observed when H/W = 2, 3. After reducing ρ from 1 to 0.5, big crown trees increase peak daytime thermal stress according to both indices when H/W = 1, 2. Small crown trees exhibit a similar PET cycle between ρ = 0.5 and ρ = 1 across various H/W, but their daytime reduction of ITS is less effective when ρ = 0.5 (H/W = 2). The discrepancies between PET and ITS are attributed to their different approaches to modelling radiation fluxes. The narrower the street, the lower the illuminance, PET, and ITS, while their increases caused by reduced ρ are limited in narrow streets. Our study informs some potential urban tree planting strategies and produces high-quality validation data for numerical simulations and theoretical models.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.