Reaching the unheard: overcoming challenges in health research with hard-to-reach populations.

PURPOSE: Addressing obstacles such as logistical complexities, social stigma, and the impact of historical traumas is essential for the successful inclusion of underrepresented groups in health research. METHODS: This article reviews engagement and interview techniques used to ethically engage recently settled Afghan refugees in Oklahoma and rural Mexican-born women in Illinois in research. The paper concludes with a reflective discussion on the challenges and lessons learned. RESULTS: Creative strategies to engage hard-to-reach populations in research included considering the participants' socioeconomic and cultural contexts in their interactions and developing community partnerships to establish trust and obtain reliable data. Other engagement strategies were communicating in the participants' preferred language, providing assistance with reading and responding to study questions for those with low literacy, employing research staff from the population of interest, and recruiting in specific locations where the populations of interest live. CONCLUSIONS: Community engagement is essential at all stages of research for building trust in hard-to-reach populations, achieving inclusivity in health research, and ensuring that interventions are culturally sensitive and effective.

Health-related quality of life in ethnically diverse Black prostate cancer survivors: a convergent parallel mixed-methods approach.

PURPOSE: This study examined the health-related quality of life (HRQoL) among ethnically diverse Black men (BM) with prostate cancer (CaP) in the United States. METHODS: A convergent parallel mixed-methods design, employing both qualitative and quantitative research, involved recruiting Black CaP survivors through multiple channels. The target population was native-born BM (NBBM), African-born BM (ABBM), and Caribbean-born BM (CBBM). QoL for all men was assessed using The Functional Assessment Cancer Therapy-Prostate (FACT-P) measure, which includes five domains: physical- (PWB), emotional- (EWB), social-(SWB), and functional-wellbeing (FWB), and a CaP subscale (PCS). A subset of men completed qualitative interviews. Demographic and clinical characteristics were also collected. RESULTS: Black CaP survivors aged 49-85 participated in the study (n = 108), with a subset (n = 31) completing a qualitative interview. Participants were mainly NBBM (72.2%) and treated with radiotherapy (51.9%). The FACT-P scale total mean score (± SD) was 114 ± 24.1 (theoretical range 0-156), with lower scores reported on the SWB, FWB, and EWB domains. The mixed-methods findings approach included meta-inferences derived from integrating the corresponding quantitative and qualitative data, covering all the domains within the FACT-P. CONCLUSION: Black CaP survivors experienced significant burdens that impacted their overall HRQoL. The analysis revealed impacts on physical, social, and emotional well-being, with variations among ethnic groups suggesting the need for culturally tailored interventions. EWB was also profoundly impacted by CaP treatment, with universal emotional burdens emphasized across all groups. Healthcare providers must recognize and address these multifaceted needs to promote better outcomes and HRQoL for Black CaP survivors.

Mobile Health Interventions for Substance Use Disorders.

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions.

Cannabis use among adults undergoing cancer treatment.

BACKGROUND: Little is known about the risks and benefits of cannabis use in the context of cancer care. This study characterized the prevalence, reasons for use, and perceived benefits of cannabis and compared symptoms and perceived risks between those who reported past 30-day cannabis use and those who did not. METHODS: Adults undergoing cancer treatment at a National Cancer Institute-designated cancer center completed measures of sociodemographic characteristics, cannabis use, use modalities, reasons for use, perceived harms/benefits of use, physical and psychological symptoms, and other substance/medication use. Analyses compared patients who used or did not use cannabis in the past 30 days. RESULTS: Participants (N = 267) were 58 years old on average, primarily female (70%), and predominantly White (88%). Over a quarter of respondents (26%) reported past 30-day cannabis use, and among those, 4.5% screened positive for cannabis use disorder. Participants who used cannabis most often used edibles (65%) or smoked cannabis (51%), and they were younger and more likely to be male, Black, and disabled, and to have lower income and Medicaid insurance than participants who did not use cannabis. Those who used cannabis reported more severe symptoms and perceived cannabis as less harmful than those who did not use cannabis. The most common medical reasons for cannabis use were pain, cancer, sleep problems, anxiety, nausea/vomiting, and poor appetite. Participants reported the greatest cannabis-related symptom relief from sleep problems, nausea/vomiting, headaches, pain, muscle spasms, and anxiety. CONCLUSIONS: Patients with cancer who used cannabis perceived benefits for many symptoms, although they showed worse overall symptomatology. PLAIN LANGUAGE SUMMARY: Among adults undergoing cancer treatment, 26% reported cannabis use in the past 30 days. Those who used cannabis were more likely to be male and disabled and to have lower income and Medicaid insurance than those who did not use cannabis. Participants most commonly reported using cannabis for pain, cancer, sleep, anxiety, and nausea/vomiting and reported the greatest perceived benefits for sleep, nausea/vomiting, headaches, pain, muscle spasms, and anxiety, yet participants who used cannabis also reported feeling worse physically and psychologically compared to those who did not use cannabis. Participants who used cannabis were more likely to report that cannabis was less risky to their health than alcohol, smoking, and opioids than those who did not use cannabis.

A Scoping Review and Meta-analysis of the Use of Remote Biochemical Verification Methods of Smoking Status in Tobacco Research.

INTRODUCTION: Increasing digital delivery of smoking cessation interventions has resulted in the need to employ novel strategies for remote biochemical verification. AIMS AND METHODS: This scoping review and meta-analysis aimed to investigate best practices for remote biochemical verification of smoking status. The scientific literature was searched for studies that reported remotely obtained (not in-person) biochemical confirmation of smoking status (ie, combustible tobacco). A meta-analysis of proportions was conducted to investigate key outcomes, which included rates of returned biological samples and the ratio of biochemically verified to self-reported abstinence rates. RESULTS: A total of 82 studies were included. The most common samples were expired air (46%) and saliva (40% of studies), the most common biomarkers were carbon monoxide (48%) and cotinine (44%), and the most common verification methods were video confirmation (37%) and mail-in samples for lab analysis (26%). Mean sample return rates determined by random-effects meta-analysis were 70% for smoking cessation intervention studies without contingency management (CM), 77% for CM studies, and 65% for other studies (eg, feasibility and secondary analyses). Among smoking cessation intervention studies without CM, self-reported abstinence rates were 21%, biochemically verified abstinence rates were 10%, and 47% of individuals who self-reported abstinence were also biochemically confirmed as abstinent. CONCLUSIONS: This scoping review suggests that improvements in sample return rates in remote biochemical verification studies of smoking status are needed. Recommendations for reporting standards are provided that may enhance confidence in the validity of reported abstinence rates in remote studies. IMPLICATIONS: This scoping review and meta-analysis included studies using remote biochemical verification to determine smoking status. Challenges exist regarding implementation and ensuring high sample return rates. Higher self-reported compared to biochemically verified abstinence rates suggest the possibility that participants in remote studies may be misreporting abstinence or not returning samples for other reasons (eg, participant burden, inconvenience). Remote biochemical confirmation of self-reported smoking abstinence should be included in smoking cessation studies whenever feasible. However, findings should be considered in the context of challenges to sample return rates. Better reporting guidelines for future studies in this area are needed.

Seeing is believing: How cannabis marketing exposure is associated with cannabis use attitudes and behavior in a permissive medical cannabis policy environment.

BACKGROUND AND OBJECTIVES: Oklahoma has a fast-growing medical cannabis industry, showing a proliferation of industry marketing. While cannabis marketing exposure (CME) is a risk factor for cannabis use and positive attitudes about use, no studies have examined the impact of CME on attitudes and use behavior in a permissive cannabis policy environment, like Oklahoma. METHODS: N = 5428 Oklahoma adults ages 18 and older completed assessments of demographics, past 30-day cannabis use, and past 30-day exposure to each of four types of cannabis marketing: outdoor (billboards, signs), social media, print (magazines), and Internet. Regression models examined associations of CME with positive attitudes towards cannabis use, cannabis harm perceptions, interest in obtaining a medical cannabis license (among nonlicensed participants), and past 30-day cannabis use. RESULTS: Three quarters (74.5%) reported any past 30-day CME. Outdoor CME was most prevalent (61.1%), followed by social media (46.5%), Internet (46.1%), and print (35.2%). Correlates of CME included younger age, higher educational attainment and income, and medical cannabis license. In adjusted regression models, past 30-day CME and number of sources of CME were associated with current cannabis use behavior, positive attitudes about cannabis, lower cannabis harm perceptions, and greater interest in obtaining a medical cannabis license. Similar associations between CME and positive attitudes about cannabis were shown among noncannabis users. DISCUSSION AND CONCLUSIONS: Public health messaging should be employed to minimize the potential adverse impacts of CME. SCIENTIFIC SIGNIFICANCE: No studies have examined correlates of CME in a rapidly growing and relatively unrestrained marketing environment.

Single-use, co-use, and polysubstance use of alcohol, tobacco, and cannabis in sexual minority and heterosexual females.

BACKGROUND AND OBJECTIVE: Alcohol, tobacco, and cannabis use are more prevalent in sexual minority females than heterosexual females, and their use is associated with adverse consequences. Identifying disparities in substance use patterns by sexual identity may inform interventions targeting this vulnerable group. This study examined differences between heterosexual and sexual minority females on patterns of past 30-day tobacco, alcohol, and cannabis use. METHODS: N = 3020 females (18.8% sexual minority) completed an online survey (September 2020-October 2021) that queried about past 30-day tobacco/nicotine (cigarettes, e-cigarettes, large cigar/LCCs, and other products), alcohol, and cannabis use. Participants were classified into one of eight patterns: no use, tobacco/nicotine-only, alcohol-only, cannabis-only, alcohol and tobacco/nicotine, tobacco/nicotine and cannabis, alcohol and cannabis, and polysubstance use. A multinomial logistic regression model examined the association between sexual identity and each substance use group, controlling for demographics. RESULTS: Across both groups, no substance use was the most common pattern. Polysubstance use was the most common substance use pattern among sexual minority females. In adjusted regression models, sexual minority females were more likely to report cannabis-only (adjusted odds ratio [AOR] = 2.58), tobacco/nicotine and cannabis co-use (AOR = 1.74), alcohol and cannabis co-use (AOR = 2.50), and polysubstance use (AOR = 2.60), compared to heterosexual females. [Correction added on 23 November 2022, after first online publication: In the preceding paragraph, the AOR and CI values were corrected.] DISCUSSION AND CONCLUSIONS: Substance use patterns that involve cannabis are more common among sexual minority females. SCIENTIFIC SIGNIFICANCE: This study extends prior research by using a large sample of females to examine differences based on sexual identity in patterns of tobacco/nicotine, alcohol, and cannabis use beyond single substance use and considers co-use and polysubstance use.

Discrimination, Substance Use, and Mental Health among Sexual and Gender Minority Adults Accessing Day Shelter Services.

BACKGROUND: Sexual and gender minority (SGM) adults are overrepresented in the population of individuals experiencing homelessness, and high rates of substance use are common in this group. Plausibly, poor mental health and discrimination may contribute to substance use among SGM adults experiencing homelessness. This study described participant characteristics, and the interrelations among sociodemographic variables, substance use, mental health, and discrimination experiences among 87 SGM adults seeking services at a day shelter in Oklahoma City, OK. Discrimination experiences were characterized by race (White vs. non-White), sex (female vs. male), sexual identity (heterosexual vs. sexual minority), and gender identity (gender conforming vs. gender minority). METHODS: Participants reported their past 30-day tobacco (cigarette/cigarillos, alternative tobacco products [ATP]), alcohol, and marijuana use, as well as everyday and lifetime major discrimination experiences, substance use problems, depression, anxiety, and post-traumatic stress disorder (PTSD). Independent samples t-tests examined differences in discrimination based on substance use and mental health. RESULTS: Participants had high rates of tobacco and marijuana use, substance use problems, depression, anxiety, and PTSD. Over 80% reported experiencing everyday or lifetime major discrimination. Depression and PTSD were associated with ATP use, and anxiety was associated with alcohol use. All mental health variables were associated with substance use problems and everyday discrimination. Depression was associated with lifetime major discrimination. CONCLUSIONS: SGM adults accessing shelter services frequently experienced discrimination and poor mental health, and substance use was common. Future research should examine the causal impact of discrimination on mental health and substance use among SGM adults experiencing homelessness.

A Mobile Just-in-Time Adaptive Intervention for Smoking Cessation: Pilot Randomized Controlled Trial.

BACKGROUND: Smartphone apps for smoking cessation could offer easily accessible, highly tailored, intensive interventions at a fraction of the cost of traditional counseling. Although there are hundreds of publicly available smoking cessation apps, few have been empirically evaluated using a randomized controlled trial (RCT) design. The Smart-Treatment (Smart-T2) app is a just-in-time adaptive intervention that uses ecological momentary assessments (EMAs) to assess the risk for imminent smoking lapse and tailors treatment messages based on the risk of lapse and reported symptoms. OBJECTIVE: This 3-armed pilot RCT aimed to determine the feasibility and preliminary efficacy of an automated smartphone-based smoking cessation intervention (Smart-T2) relative to standard in-person smoking cessation clinic care and the National Cancer Institute's free smoking cessation app, QuitGuide. METHODS: Adult smokers who attended a clinic-based tobacco cessation program were randomized into groups and followed for 13 weeks (1 week prequitting through 12 weeks postquitting). All study participants received nicotine patches and gum and were asked to complete EMAs five times a day on study-provided smartphones for 5 weeks. Participants in the Smart-T2 group received tailored treatment messages after the completion of each EMA. Both Smart-T2 and QuitGuide apps offer on-demand smoking cessation treatment. RESULTS: Of 81 participants, 41 (50%) were women and 55 (68%) were white. On average, participants were aged 49.6 years and smoked 22.4 cigarettes per day at baseline. A total of 17% (14/81) of participants were biochemically confirmed 7-day point prevalence abstinent at 12 weeks postquitting (Smart-T2: 6/27, 22%, QuitGuide: 4/27, 15%, and usual care: 4/27, 15%), with no significant differences across groups (P>.05). Participants in the Smart-T2 group rated the app positively, with most participants agreeing that they can rely on the app to help them quit smoking, and endorsed the belief that the app would help them stay quit, and these responses were not significantly different from the ratings given by participants in the usual care group. CONCLUSIONS: Dynamic smartphone apps that tailor intervention content in real time may increase user engagement and exposure to treatment-related materials. The results of this pilot RCT suggest that smartphone-based smoking cessation treatments may be capable of providing similar outcomes to traditional, in-person counseling. TRIAL REGISTRATION: ClinicalTrials.gov NCT02930200; https://clinicaltrials.gov/show/NCT02930200.

Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse.

Introduction: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention. Methods: In a randomized-controlled smoking cessation trial, smartphone-based EMAs were collected from 92 participants following a scheduled quit date. This secondary analysis utilized elastic net-penalized cox proportional hazards regression and model approximation via backward elimination to (1) optimize a predictive model of time to first lapse and (2) simplify that model to its core constituent predictors to maximize parsimony and generalizability. Results: Elastic net proportional hazards regression selected 17 of 26 possible predictors from 2065 EMAs to model time to first lapse. The predictors with the highest magnitude regression coefficients were having consumed alcohol in the past hour, being around and interacting with a smoker, and having cigarettes easily available. This model was reduced using backward elimination, retaining five predictors and approximating to 93.9% of model fit. The retained predictors included those mentioned above as well as feeling irritable and being in areas where smoking is either discouraged or allowed (as opposed to not permitted). Conclusions: The strongest predictors of smoking lapse were environmental in nature (e.g., being in smoking-permitted areas) as opposed to internal factors such as psychological affect. Interventions may be improved by a renewed focus of interventions on these predictors. Implications: The present study demonstrated the utility of machine learning algorithms to optimize the prediction of time to smoking lapse using EMA data. The two models generated by the present analysis found that environmental factors were most strongly related to smoking lapse. The results support the use of machine learning algorithms to investigate intensive longitudinal data, and provide a foundation for the development of highly tailored, just-in-time interventions that can target on multiple antecedents of smoking lapse.

Health-Related Quality of Life Among Homeless Smokers: Risk and Protective Factors of Latent Class Membership.

Health-related quality of life (HRQoL) is a multidimensional assessment of well-being and health status. Most work in this area assumes that HRQoL is a homogenous construct; however, it is possible HRQoL subgroups may exist. The purpose of the study was to characterize common classes of HRQoL among adult, homeless smokers, a particularly vulnerable group of the larger population, and to evaluate risk and protective factors of HRQoL class membership. Homeless smokers (N = 456; 65.1% male; Mage = 43.19 years [SD = 11.77]) completed self-report measures of sociodemographics, smoking characteristics, anxiety sensitivity, stress, social support, and the Center for Disease Control (CDC) four-item HRQoL measure. A latent class analysis was conducted for HRQoL. Multinomial regression models were used to simultaneously test correlates of class membership. A three-class solution, consisting of poor HRQoL, moderate HRQoL, and excellent HRQoL, demonstrated superior fit. Correlates of class membership included sex, age, lifetime months of being homeless, smoking characteristics, anxiety sensitivity, stress, and social support. The current findings provide novel evidence for three distinct classes of HRQoL among homeless smokers. Results suggest that older smokers with greater emotional distress, as evidenced by greater anxiety sensitivity, greater stress, and less social support, may be particularly vulnerable to poorer HRQoL.

Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation.

The application of sophisticated analytical methods to intensive longitudinal data, collected with ecological momentary assessments (EMA), has helped researchers better understand smoking behaviors after a quit attempt. Unfortunately, the wealth of information captured with EMAs is typically underutilized in practice. Thus, novel methods are needed to extract this information in exploratory research studies. One of the main objectives of intensive longitudinal data analysis is identifying relations between risk factors and outcomes of interest. Our goal is to develop and apply expectation maximization variable selection for Bayesian multistate Markov models with interval-censored data to generate new insights into the relation between potential risk factors and transitions between smoking states. Through simulation, we demonstrate the effectiveness of our method in identifying associated risk factors and its ability to outperform the LASSO in a special case. Additionally, we use the expectation conditional-maximization algorithm to simplify estimation, a deterministic annealing variant to reduce the algorithm's dependence on starting values, and Louis's method to estimate unknown parameter uncertainty. We then apply our method to intensive longitudinal data collected with EMA to identify risk factors associated with transitions between smoking states after a quit attempt in a cohort of socioeconomically disadvantaged smokers who were interested in quitting.

The Time-Varying Relations Between Risk Factors and Smoking Before and After a Quit Attempt.

Introduction: Intensive longitudinal data (ILD) collected with ecological momentary assessments (EMAs) can provide a rich resource for understanding the relations between risk factors and smoking in the time surrounding a cessation attempt. Methods: Participants (N = 142) were smokers seeking treatment at a safety-net hospital smoking cessation clinic who were randomly assigned to receive standard clinic care (ie, counseling and cessation medications) or standard care plus small financial incentives for biochemically confirmed smoking abstinence. Participants completed EMAs via study provided smartphones several times per day for 14 days (1 week prequit through 1 week postquit). EMAs assessed current contextual factors including environmental (eg, easy access to cigarettes, being around others smoking), cognitive (eg, urge to smoke, stress, coping expectancies, cessation motivation, cessation self-efficacy, restlessness), behavioral (ie, recent smoking and alcohol consumption), and affective variables. Temporal relations between risk factors and smoking were assessed using a logistic time-varying effect model. Results: Participants were primarily female (57.8%) and Black (71.8%), with an annual household income of <$20000 per year (71.8%), who smoked 17.6 cigarettes per day (SD = 8.8). Individuals assigned to the financial incentives group had decreased odds of smoking compared with those assigned to usual care beginning 3 days before the quit attempt and continuing throughout the first week postquit. Environmental, cognitive, affective, and behavioral variables had complex time-varying impacts on smoking before and after the scheduled quit attempt. Conclusions: Knowledge of time-varying effects may facilitate the development of interventions that target specific psychosocial and behavioral variables at critical moments in the weeks surrounding a quit attempt. Implications: Previous research has examined time-varying relations between smoking and negative affect, urge to smoke, smoking dependence, and certain smoking cessation therapies. We extend this work using ILD of unexplored variables in a socioeconomically disadvantaged sample of smokers seeking cessation treatment. These findings could be used to inform ecological momentary interventions that deliver treatment resources (eg, video- or text-based content) to individuals based upon critical variables surrounding their attempt.

Cigarette Purchasing Patterns, Readiness to Quit, and Quit Attempts Among Homeless Smokers.

INTRODUCTION: Cigarette purchasing patterns may be linked with greater readiness to make a quit attempt and more quit attempts among domiciled samples. However, little is known about the cigarette purchasing patterns of homeless smokers or their potential relations to quitting intention and behaviors. This study redressed this gap among a convenience sample of homeless adult smokers from a large shelter in Dallas, Texas. METHODS: Participants (N = 207; Mage = 43; 71.5% male) smoked ≥100 cigarettes over the lifetime and endorsed current daily smoking. Variables assessed included cigarette dependence (time to first cigarette of the day), monthly income, quantity of cigarettes most recently purchased, average money spent on cigarettes weekly, readiness/motivation to quit smoking, and the number intentional quit attempts lasting ≥24h in the past year. Regression analyses were conducted to characterize associations of cigarette purchasing patterns with readiness to quit and quit attempts controlling for sex, age, cigarette dependence, and income. RESULTS: Most participants purchased cigarettes by the pack (61.4%), and more than half the sample spent ≤$20 on cigarettes per week. Results indicated that spending less money per week on cigarettes was associated with greater readiness to quit (P = .016), even when controlling for income, cigarette dependence, and other covariates. Stratified analyses indicated that this association was significant only for homeless smokers reporting no regular monthly income. CONCLUSIONS: Homeless daily smokers with no reported income who spend little money on cigarettes may make particularly apt targets for cessation interventions due to potential associations with quitting motivation. IMPLICATIONS: Adults who are homeless smoke at greater rates and quit at lower rates than domiciled adults, leading to significant smoking-related health disparities among this group. Findings suggest that cigarette purchasing patterns are linked with readiness to quit smoking among smokers who are homeless. Results elucidate one of the myriad factors that contribute to tobacco-related disparities among this group and findings may have implications for cessation interventions in homeless shelters and other contexts where resources are limited.

Ecological Momentary Assessment of Physical Activity: Validation Study.

BACKGROUND: Ecological momentary assessment (EMA) may elicit physical activity (PA) estimates that are less prone to bias than traditional self-report measures while providing context. OBJECTIVES: The objective of this study was to examine the convergent validity of EMA-assessed PA compared with accelerometry. METHODS: The participants self-reported their PA using International Physical Activity Questionnaire (IPAQ) and Behavioral Risk Factor Surveillance System (BRFSS) and wore an accelerometer while completing daily EMAs (delivered through the mobile phone) for 7 days. Weekly summary estimates included sedentary time and moderate-, vigorous-, and moderate-to vigorous-intensity physical activity (MVPA). Spearman coefficients and Lin's concordance correlation coefficients (LCC) examined the linear association and agreement for EMA and the questionnaires as compared with accelerometry. RESULTS: Participants were aged 43.3 (SD 13.1) years, 51.7% (123/238) were African American, 74.8% (178/238) were overweight or obese, and 63.0% (150/238) were low income. The linear associations of EMA and traditional self-reports with accelerometer estimates were statistically significant (P<.05) for sedentary time (EMA: ρ=.16), moderate-intensity PA (EMA: ρ=.29; BRFSS: ρ=.17; IPAQ: ρ=.24), and MVPA (EMA: ρ=.31; BRFSS: ρ=.17; IPAQ: ρ=.20). Only EMA estimates of PA were statistically significant compared with accelerometer for agreement. CONCLUSIONS: The mobile EMA showed better correlation and agreement to accelerometer estimates than traditional self-report methods. These findings suggest that mobile EMA may be a practical alternative to accelerometers to assess PA in free-living settings.

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt.

BACKGROUND: Mobile phone‒based real-time ecological momentary assessments (EMAs) have been used to record health risk behaviors, and antecedents to those behaviors, as they occur in near real time. OBJECTIVE: The objective of this study was to determine if intensive longitudinal data, collected via mobile phone, could be used to identify imminent risk for smoking lapse among socioeconomically disadvantaged smokers seeking smoking cessation treatment. METHODS: Participants were recruited into a randomized controlled smoking cessation trial at an urban safety-net hospital tobacco cessation clinic. All participants completed in-person EMAs on mobile phones provided by the study. The presence of six commonly cited lapse risk variables (ie, urge to smoke, stress, recent alcohol consumption, interaction with someone smoking, cessation motivation, and cigarette availability) collected during 2152 prompted or self-initiated postcessation EMAs was examined to determine whether the number of lapse risk factors was greater when lapse was imminent (ie, within 4 hours) than when lapse was not imminent. Various strategies were used to weight variables in efforts to improve the predictive utility of the lapse risk estimator. RESULTS: Participants (N=92) were mostly female (52/92, 57%), minority (65/92, 71%), 51.9 (SD 7.4) years old, and smoked 18.0 (SD 8.5) cigarettes per day. EMA data indicated significantly higher urges (P=.01), stress (P=.002), alcohol consumption (P<.001), interaction with someone smoking (P<.001), and lower cessation motivation (P=.03) within 4 hours of the first lapse compared with EMAs collected when lapse was not imminent. Further, the total number of lapse risk factors present within 4 hours of lapse (mean 2.43, SD 1.37) was significantly higher than the number of lapse risk factors present during periods when lapse was not imminent (mean 1.35, SD 1.04), P<.001. Overall, 62% (32/52) of all participants who lapsed completed at least one EMA wherein they reported ≥3 lapse risk factors within 4 hours of their first lapse. Differentially weighting lapse risk variables resulted in an improved risk estimator (weighted area=0.76 vs unweighted area=0.72, P<.004). Specifically, 80% (42/52) of all participants who lapsed had at least one EMA with a lapse risk score above the cut-off within 4 hours of their first lapse. CONCLUSIONS: Real-time estimation of smoking lapse risk is feasible and may pave the way for development of mobile phone‒based smoking cessation treatments that automatically tailor treatment content in real time based on presence of specific lapse triggers. Interventions that identify risk for lapse and automatically deliver tailored messages or other treatment components in real time could offer effective, low cost, and highly disseminable treatments to individuals who do not have access to other more standard cessation treatments.

An Ecological Momentary Intervention for Smoking Cessation: Evaluation of Feasibility and Effectiveness.

BACKGROUND: Despite substantial public health progress in reducing the prevalence of smoking in the United States overall, smoking among socioeconomically disadvantaged adults remains high. OBJECTIVE: To determine the feasibility and preliminary effectiveness of a novel smartphone-based smoking cessation app designed for socioeconomically disadvantaged smokers. METHODS: Participants were recruited from a safety-net hospital smoking cessation clinic in Dallas, Texas, and were followed for 13 weeks. All participants received standard smoking cessation clinic care (ie, group counseling and cessation pharmacotherapy) and a smartphone with a novel smoking cessation app (ie, Smart-T). The Smart-T app prompted 5 daily ecological momentary assessments (EMAs) for 3 weeks (ie, 1 week before cessation and 2 weeks after cessation). During the precessation period, EMAs were followed by messages that focused on planning and preparing for the quit attempt. During the postcessation period, participant responses to EMAs drove an algorithm that tailored messages to the current level of smoking lapse risk and currently present lapse triggers (eg, urge to smoke, stress). Smart-T offered additional intervention features on demand (eg, one-click access to the tobacco cessation quitline; "Quit Tips" on coping with urges to smoke, mood, and stress). RESULTS: Participants (N=59) were 52.0 (SD 7.0) years old, 54% (32/59) female, and 53% (31/59) African American, and 70% (40/57) had annual household income less than US $16,000. Participants smoked 20.3 (SD 11.6) cigarettes per day and had been smoking for 31.6 (SD 10.9) years. Twelve weeks after the scheduled quit date, 20% (12/59) of all participants were biochemically confirmed abstinent. Participants responded to 87% of all prompted EMAs and received approximately 102 treatment messages over the 3-week EMA period. Most participants (83%, 49/59) used the on-demand app features. Individuals with greater nicotine dependence and minority race used the Quit Tips feature more than their counterparts. Greater use of the Quit Tips feature was linked to nonabstinence at the 2 (P=.02), 4 (P<.01), and 12 (P=.03) week follow-up visits. Most participants reported that they actually used or implemented the tailored app-generated messages and suggestions (83%, 49/59); the app-generated messages were helpful (97%, 57/59); they would like to use the app in the future if they were to lapse (97%, 57/59); and they would like to refer friends who smoke to use the Smart-T app (85%, 50/59). A minority of participants (15%, 9/59) reported that the number of daily assessments (ie, 5) was "too high." CONCLUSIONS: This novel just-in-time adaptive intervention delivered an intensive intervention (ie, 102 messages over a 3-week period), was well-liked, and was perceived as helpful and useful by socioeconomically disadvantaged adults who were seeking smoking cessation treatment. Smartphone apps may be used to increase treatment exposure and may ultimately reduce tobacco-related health disparities among socioeconomically disadvantaged adults.

Association Between Sitting Time and Cardiometabolic Risk Factors After Adjustment for Cardiorespiratory Fitness, Cooper Center Longitudinal Study, 2010-2013.

INTRODUCTION: Objective estimates, based on waist-worn accelerometers, indicate that adults spend over half their day (55%) in sedentary behaviors. Our study examined the association between sitting time and cardiometabolic risk factors after adjustment for cardiorespiratory fitness (CRF). METHODS: A cross-sectional analysis was conducted with 4,486 men and 1,845 women who reported daily estimated sitting time, had measures for adiposity, blood lipids, glucose, and blood pressure, and underwent maximal stress testing. We used a modeling strategy using logistic regression analysis to assess CRF as a potential effect modifier and to control for potential confounding effects of CRF. RESULTS: Men who sat almost all of the time (about 100%) were more likely to be obese whether defined by waist girth (OR, 2.61; 95% CI, 1.25-5.47) or percentage of body fat (OR, 3.33; 95% CI, 1.35-8.20) than were men who sat almost none of the time (about 0%). Sitting time was not significantly associated with other cardiometabolic risk factors after adjustment for CRF level. For women, no significant associations between sitting time and cardiometabolic risk factors were observed after adjustment for CRF and other covariates. CONCLUSION: As health professionals struggle to find ways to combat obesity and its health effects, reducing sitting time can be an initial step in a total physical activity plan that includes strategies to reduce sedentary time through increases in physical activity among men. In addition, further research is needed to elucidate the relationships between sitting time and CRF for women as well as the underlying mechanisms involved in these relationships.

Impact of a Mobile Phone Intervention to Reduce Sedentary Behavior in a Community Sample of Adults: A Quasi-Experimental Evaluation.

BACKGROUND: Greater time spent sedentary is linked with increased risk of breast, colorectal, ovarian, endometrial, and prostate cancers. Given steadily increasing rates of mobile phone ownership, mobile phone interventions may have the potential to broadly influence sedentary behavior across settings. OBJECTIVE: The purpose of this study was to examine the short-term impact of a mobile phone intervention that targeted sedentary time in a diverse community sample. METHODS: Adults participated in a quasi-experimental evaluation of a mobile phone intervention designed to reduce sedentary time through prompts to interrupt periods of sitting. Participants carried mobile phones and wore accelerometers for 7 consecutive days. Intervention participants additionally received mobile phone prompts during self-reported sitting and information about the negative health impact of prolonged sedentariness. The study was conducted from December 2012 to November 2013 in Dallas, Texas. Linear mixed model regression analyses were conducted to evaluate the influence of the intervention on daily accelerometer-determined estimates of sedentary and active time. RESULTS: Participants (N=215) were predominantly female (67.9%, 146/215) and nonwhite (black: 50.7%, 109/215; Latino: 12.1%, 26/215; other: 5.6%, 12/215). Analyses revealed that participants who received the mobile phone intervention had significantly fewer daily minutes of sedentary time (B=-22.09, P=.045) and more daily active minutes (B=23.01, P=.04) than control participants. CONCLUSIONS: A simple mobile phone intervention was associated with engaging in less sedentary time and more physical activity. Findings underscore the potential impact of mobile phone interventions to positively influence sedentary behavior and physical activity.

Financial incentives for abstinence among socioeconomically disadvantaged individuals in smoking cessation treatment.

OBJECTIVES: We evaluated the effectiveness of offering adjunctive financial incentives for abstinence (contingency management [CM]) within a safety net hospital smoking cessation program. METHODS: We randomized participants (n = 146) from a Dallas County, Texas, Tobacco Cessation Clinic from 2011 to 2013 to usual care (UC; cessation program; n = 71) or CM (UC + 4 weeks of financial incentives; n = 75), and followed from 1 week before the quit date through 4 weeks after the quit date. A subset (n = 128) was asked to attend a visit 12 weeks after the scheduled quit date. RESULTS: Participants were primarily Black (62.3%) or White (28.1%) and female (57.5%). Most participants were uninsured (52.1%) and had an annual household income of less than $12 000 (55.5%). Abstinence rates were significantly higher for those assigned to CM than UC at all visits following the quit date (all Ps < .05). Point prevalence abstinence rates in the CM and UC groups were 49.3% versus 25.4% at 4 weeks after the quit date and 32.8% versus 14.1% at 12 weeks after the quit date. CM participants earned an average of $63.40 ($150 possible) for abstinence during the first 4 weeks after the scheduled quit date. CONCLUSIONS: Offering small financial incentives for abstinence might be an effective means to improve abstinence rates among socioeconomically disadvantaged individuals participating in smoking cessation treatment.