Ecological momentary assessment of smoking behaviors in native and converted intermittent smokers

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.

Intermittent nicotine access is as effective as continuous access in promoting nicotine seeking and taking in rats

RATIONALE

Nicotine is a principal psychoactive agent in tobacco, contributing to tobacco's addictive potential. Preclinical studies on the effects of voluntary nicotine intake typically use self-administration procedures that provide continuous nicotine access during each self-administration session. However, many smokers consume cigarettes intermittently rather than continuously throughout each day. For drugs including cocaine and opioids, research in laboratory rats shows that intermittent intake can be more effective than continuous intake in producing patterns of drug use relevant to addiction.

OBJECTIVE

We determined how intermittent versus continuous nicotine self-administration influences nicotine seeking and taking behaviours.

METHODS

Female and male rats had continuous (i.e., Long Access; LgA, 6 h/day) or intermittent (IntA; 12 min ON, 60 min OFF, for 6 h/day) access to intravenous nicotine (15 µg/kg/infusion), for 12 daily sessions. We then assessed intake, responding for nicotine under a progressive ratio schedule of drug reinforcement and cue- and nicotine-induced reinstatement of drug seeking. We also estimated nicotine pharmacokinetic parameters during LgA and IntA self-administration.

RESULTS

Overall, LgA rats took twice more nicotine than did IntA rats, yielding more sustained increases in estimated brain concentrations of the drug. However, the two groups showed similar motivation to seek and take nicotine, as measured using reinstatement and progressive ratio procedures, respectively.

CONCLUSIONS

Intermittent nicotine use is just as effective as continuous use in producing addiction-relevant behaviours, despite significantly less nicotine exposure. This has implications for modeling nicotine self-administration patterns in human smokers and resulting effects on brain and behaviour.

Characterizing nicotine exposure among a community sample of non-daily smokers in the United States

Background

Over one-quarter of all smokers in the United States identify as non-daily smokers and this number is projected to rise. Unlike daily smokers who typically maintain consistent levels of nicotine exposure with regular smoking, non-daily smokers have variable patterns of smoking that likely result in high intraindividual variability in nicotine intake. The current study aimed to characterize the weekly intraindividual variability in cotinine and identify smoking-related predictors in nondaily smokers.

Methods

An ecological momentary assessment of 60 non-daily smokers ages 24–57 years was conducted over a consecutive 7-day at-home protocol to log each smoking session, assessments of mood and social activity during smoking, and collection of daily saliva samples in a convenience sample from Pennsylvania, USA. Hierarchical linear regression analyses were conducted to determine the effects of smoking characteristics on total cotinine exposure measured by pharmacokinetic area under the curve and the range, maximum, and minimum cotinine values during the week controlling for demographic variables.

Results

The mean daily cotinine level was 119.2 ng/ml (SD = 168.9) with individual values that ranged from nondetectable to 949.6 ng/ml. Menthol predicted increased total cotinine levels (P < 0.05). Shorter time to the first cigarette of the day predicted significantly higher minimum (P < 0.05), maximum (P < 0.05), and total cotinine values (P < 0.05) after controlling for covariates. Negative emotions and social interactions with others were also significantly associated with higher cotinine metrics. There was no significant effect of the nicotine metabolite ratio.

Conclusions

Our findings highlight the variability in nicotine exposure across days among non-daily smokers and point to the role of smoking context in nicotine exposure. The findings suggest the need to develop better assessment methods to determine health and dependence risk and personalized cessation interventions for this heterogeneous and growing group of smokers.

Differences in Distress Intolerance Among Daily and Intermittent Smokers

INTRODUCTION

Distress intolerance is an important risk factor for smokers. Smokers have greater problems tolerating distress than nonsmokers, and distress intolerance is theoretically an important predictor of early lapse. However, much of the distress intolerance research has been conducted on daily smokers. Understanding distress intolerance in nondaily or intermittent smokers may help elucidate whether distress intolerance is a function of current smoking habits.

AIMS AND METHODS

Daily (n = 36) and intermittent (n = 28) smokers completed behavioral distress intolerance tasks (breath holding, mirror tracing persistence, and image persistence) along with self-report measures of both general and smoking-specific distress intolerance. They also completed 1 week of ecological momentary assessment where positive and negative affect were assessed along with momentary distress intolerance, at both random times (7×/day) and immediately prior to smoking a cigarette.

RESULTS

Results found no differences between intermittent and daily smokers on behavioral distress intolerance tasks or general self-reported distress intolerance. Daily smokers reported greater self-reported smoking-specific distress intolerance compared to intermittent smokers. In addition, across both smoker groups, momentary distress intolerance was higher at smoking compared to random sessions, and low positive affect predicted greater momentary distress intolerance specifically for intermittent smokers prior to smoking.

CONCLUSIONS

The lack of differences between daily and intermittent smokers on general distress intolerance measures suggests that distress intolerance abilities and self-perceptions are not a function of higher levels of current smoking. However, the contextual variation in momentary distress intolerance is worth further exploration in both daily and intermittent smokers.

IMPLICATIONS

The overall lack of differences between intermittent and daily smokers on distress intolerance tasks and self-report measures suggests that daily smoking is not associated with lower abilities to manage or tolerate distress at the individual difference level. However, understanding fluctuations in distress intolerance across time and context is crucial, as smokers' perceptions of their abilities to manage distress shift based on affect and smoking contexts. Stabilizing or increasing self-efficacy in tolerating distress during daily life is likely an important avenue for future research.

A Systematic Review of Recent Methodological Approaches for Using Ecological Momentary Assessment to Examine Outcomes in U.S. Based HIV Research

PURPOSE OF REVIEW

In recent years, researchers have been adopting and using ecological momentary assessment (EMA) methods via technology devices for real-time measurement of exposures and outcomes in HIV research. To assess and critically evaluate how EMA methods are currently being used in HIV research, we systematically reviewed recent published literature (October 2017-October 2019) and searched select conference databases for 2018 and 2019.

RECENT FINDINGS

Our searches identified 8 published articles that used EMA via smartphone app, a handheld Personal Digital Assistant, and web-based survey programs for real-time measurement of HIV-related exposures and outcomes in behavioral research. Overall trends include use of EMA and technology devices to address substance use, HIV primary prevention (e.g., condom use and preexposure prophylaxis), and HIV treatment (medication adherence). This review supports the use of EMA methods in HIV research and recommends that researchers use EMA methods to measure psychosocial factors and social contexts and with Black and Latinx samples of gay and bisexual men, transgender women, and cisgendered women to reflect current HIV disparities in the U.S.A.

A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors

A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.

A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors

A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory inductive plethysmography (RIP) has been employed to track breathing and to identify characteristic breathing pattern specific to smoking. Pattern recognition algorithms such as Support Vector Machine (SVM), Hidden Markov Model, Decision tree, or ensemble approaches have been used to identify smoke inhalations. However, no deep learning approaches, which have been proved effective to many time series datasets, have ever been tested yet. Hence, a Convolutional Neural Network (CNN) and Long Term Short Memory (LSTM) based approach is presented in this paper to detect smoke inhalations in the breathing signal. To illustrate the effectiveness of this deep learning approach, a traditional machine learning (SVM) based approach was used for comparison. On the validation dataset of 120 smoking sessions performed in a laboratory setting by 30 moderate-to-heavy smokers, the CNN-LSTM approach achieved an F1-score of 72% in leave-one-subject-out (LOSO) cross-validation method whereas the classical SVM approach scored 63%. These results suggest that deep learning-based approaches might provide a better analytical method for detection of smoke inhalations than more conventional machine learning approaches.

Heterogeneity of intermittent smokers in a Hispanic college student sample

Hispanics are more likely to be daily light smokers (DLS) and intermittent smokers (ITS) than non-Hispanic whites. Although daily light (≤10 cigarettes per day [CPD]) and intermittent (nondaily) smoking have increased in recent years, few studies have compared DLS and ITS, especially within a Hispanic sample. The primary aims of this study were to investigate differences between DLS and ITS, and within ITS, differences between converted ITS (CITS; previously smoked daily for ≥6 months) and native ITS (NITS; never smoked daily) in a Hispanic college student sample (M_age = 23.74, SD = 5.17; 58.1% male). Analyses were conducted using baseline data from a larger study that evaluated attitudes toward tobacco free campus policies in a U.S. university on the border with México. This study included data from 45 DLS and 216 ITS (CITS: n = 77, NITS: n = 139; N = 261). Compared to DLS, ITS were younger (on average), less likely to identify as smokers, smoked on fewer days in the past month, smoked fewer cigarettes on smoking days, and reported less nicotine dependence. Compared to CITS, NITS were younger, less likely to self-identify as smokers, smoked on fewer days in the past month, smoked fewer CPD on smoking days, and were less dependent on nicotine. Given the similarities between current and past findings (suggesting that CITS are in between DLS and NITS-regarding smoking behavior), these data suggest a similar pattern likely exists also among Hispanic smokers. Additionally, the absence of some previously observed differences is relevant in characterizing this particular Hispanic college sample. These findings provide further insight for the tailoring of interventions that target Hispanic DLS, CITS and NITS).

Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.