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. 2017 Oct 4;20(10):1231–1236. doi: 10.1093/ntr/ntx225

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

Matthew D Koslovsky 1,✉, Emily T Hébert 2, Michael D Swartz 1, Wenyaw Chan 1, Luis Leon-Novelo 1, Anna V Wilkinson 3, Darla E Kendzor 2,4, Michael S Businelle 2,4
PMCID: PMC6121909  PMID: 29059413

Abstract

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.

Introduction

Tobacco use is the leading cause of preventable death in the United States.1 Unfortunately, smoking cessation attempts are typically unsuccessful.2 While effective treatments increase abstinence rates, relapse is more common than cessation.3 Low success rates have been attributed to our misunderstanding of the addiction process.3 For instance, the urge to smoke is considered a defining characteristic of addiction.4 However, its relation with smoking is inconsistent across studies.5 These inconsistencies may be related to differences in assessment times (ie, how the measured characteristic relates to smoking may vary before and after a quit attempt) and to the length of the study assessment period (ie, most relapses occur in the first 5–10 days after a quit attempt).5,6 Thus, addiction researchers seek to develop a more complex understanding of the various influences on behavior change.7

Intensive longitudinal data (ILD), which may be collected via ecological momentary assessments (EMAs), have provided a rich resource for understanding smoking in the time surrounding a quit attempt.7–12 EMA methods involve repeated assessment of individuals in their natural environments in near real-time.13 Thus, EMAs can capture multiple psychological, emotional, and environmental factors that relate to an individual’s smoking behavior in the natural environment. Gaining a better understanding of the relations between risk factors and smoking before and after a quit attempt may help public health researchers design more effective smoking cessation interventions. Nevertheless, EMA methods remain underused,7 perhaps due in part to the need for novel analytical approaches capable of extracting the information embedded in the data.10 The objective of the current study was to obtain a better understanding of the relation between risk factors and smoking in the critical period surrounding a quit attempt.

Time-Varying Effect Models

ILD analyses often aim to identify or re-affirm complex relations over time between potential risk factors and behavioral outcomes.14 The application of time-varying effect models (TVEM) has become an increasingly popular approach to understanding the dynamic relation between a risk factor and a smoking outcome over time.7–11 While most TVEM studies have focused on urge to smoke as an outcome,7–9,11 Vasilenko et al.10 targeted the time-varying relations between smoking lapse and urge to smoke, cessation treatments, baseline smoking dependence, and negative affect in the 2 weeks after a quit attempt. They found that negative affect had an increasingly positive relation with smoking, starting 7 days after the quit date, and craving had a convex and concave relation with smoking, during the first and second week after the quit date, respectively. The findings indicate that risk factors may differentially affect smoking behaviors around the quit date.15 Thus, it is of interest to study effects of particular risk factors before a quit attempt as well as after. Additionally, there are various other risk factors implicated in smoking behaviors beyond what was explored by Vasilenko et al.10 Therefore, in addition to reproducing previous results (ie, time-varying relations between smoking, urge to smoke, and negative affect), it is important to explore potential time-varying relations between additional risk factors and smoking. This approach would capture changes in the relations between risk factors and smoking before and after a scheduled quit date.

Risk Factors for Smoking

A number of psychological and environmental factors have been shown to be associated with smoking.16–18 For example, psychological factors such as urge to smoke,5 negative affect,19 restlessness,20 stress,21 motivation to quit,22 self-efficacy to quit,23,24 and positive smoking expectancies19,22 have been shown to be associated with smoking. Declining restlessness and stress, as well as increasing positive coping expectancies before a quit attempt have predicted quit date abstinence.22 Similarly, environmental factors such as cigarette availability, being around other smokers, and smoking restrictions in an individual’s current location have been associated with smoking.21 However, few studies have examined time-varying relations between smoking risk factors and smoking before and after a quit attempt.9 In this study, we explored the time-varying relations between risk factors and smoking behaviors over a 2-week period (1 week before cessation through 1 week after a scheduled quit attempt) using a logistic TVEM, controlling for time-independent, baseline measures.

Methods

Procedures

Details of the data collection procedures and study design are reported elsewhere.25 In brief, from August 2011 to April 2013, potential participants were recruited during an initial orientation visit at a Dallas, Texas-based safety-net hospital smoking cessation program. Interested individuals completed the informed consent document and were screened for study eligibility. Individuals were eligible if they were 18 years or older, able to read English at the seventh grade level or higher, smoked five or more cigarettes per day, registered an expired breath sample with a carbon monoxide (CO) level at 8 parts per million (ppm) or above, and were willing to quit smoking 1 week after the baseline visit. Of those screened, 146 individuals met the eligibility criteria and were randomized into treatment groups. Those excluded from the study did not differ from participants in terms of age, race, or gender, but did have lower literacy levels (p < .001) and CO readings (p < .001).25 Participants were randomly assigned to receive usual smoking cessation care from the safety-net hospital (n = 71), which included group counseling and pharmacotherapy, or usual care combined with financial incentives for biochemically verified abstinence (ie, contingency management [CM]; n = 75). Individuals were informed of their treatment assignment at baseline, and those in the CM group were told they would receive financial incentives if they exhibited ≈12 h of abstinence on their scheduled quit date and 7 days of abstinence at the 1-, 2-, 3-, and 4-week follow-up visits. At the six clinic visits (weekly from 1 week prequit to 4 weeks postquit and again at 12 weeks postquit), abstinence was determined by biochemically verified self-report (self-reported abstinence and CO < 8 ppm). Baseline measures were collected a week before the scheduled quit date, and individuals were shown how to complete EMAs on a study-provided smartphone. During the 2-week EMA period (1 week before cessation through 1 week after a scheduled quit attempt), individuals were prompted by the smartphone to complete four random EMAs throughout the day. The phone audibly and visually cued each random assessment for 30 s. If the participant did not respond after three prompts, the assessment was recorded as missed. Overall, participants were compliant with EMA prompts, and responded to 81% of all random assessments.

Measures

At baseline, all participants reported on their age, sex, race/ethnicity, and smoking history (independent variables, Supplementary Table S1). During each EMA, participants were asked if they had smoked since their last assessment (dependent variable), whether they had consumed alcohol in the past hour, as well as questions about psychological, social, and environmental factors that could contribute to increased lapse risk (independent variables, Supplementary Table S2).

Psychological/Affective Factors

On a scale from 1 (strongly disagree) to 5 (strongly agree), participants rated their urge to smoke, and their feelings of restlessness, stress, irritability, frustration/anger, sadness, worry, misery, happiness, and calmness. In addition, participants rated their positive coping expectancies (eg, “I am confident that I could do something other than smoke to improve my mood”), positive smoking expectancies (eg, I am confident smoking would improve my mood”), motivation to quit, and confidence in their ability to avoid smoking.

Social/Environmental Factors

Participants were asked whether or not there were other smokers around (yes/no), whether or not cigarettes were easily available (1 = not at all to 5 = easily available), whether or not smoking was allowed where they were (smoking is forbidden/discouraged/allowed), and whether or not they were interacting with someone who was smoking (yes/no).

Analytical Approach

A multivariable logistic TVEM was used to examine the temporal effects of the selected risk factors on momentary smoking (defined as reporting having smoked even one puff since the last EMA) from 1 week before cessation through 1 week after the quit date. We used the R package mgcv to fit the TVEM for binary outcomes26 and estimated time-varying effects using penalized cubic splines. To set the number of knots for the splines, we followed the approach proposed by Tan et al.27 This approach iteratively adjusts the number of knots in the model until the coefficient functions stabilize. The model with the smallest Akaike information criterion was selected as the final model for analysis.28 The relation of each risk factor with momentary smoking was allowed to vary over time. Additionally, we controlled for gender, race/ethnicity, years of education, and heaviness of smoking index (HSI) with time-invariant effects. See Supplementary Material for an example of our approach.

Results

A total of 5984 (2752 prequit and 3232 postquit date) random EMAs from 142 individuals had complete responses to all items and were used in the current analyses. Participants were primarily female (57.8%), Black (71.8%), were 52.3 years old (SD = 7.2), had a total household income of less than $20000 per year (71.8%), had completed on average 12 years of education (SD = 2.0), and smoked an average of 17.6 cigarettes per day (SD = 8.8).

The results of the TVEM are plotted in Figure 1, a–o. In these plots, time-varying effects are significant at a moment in time if the 95% confidence bands (shown in grey) do not include the dashed line (odds ratio = 1) at that particular time. Thus, odds ratios above the dashed line show a positive association with momentary smoking. Odds ratios below the dashed line signify a negative association.

Figure 1.

Figure 1.

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(a–i) Time-varying effect of risk factors on smoking. (j–o) Time-varying effect of risk factors on smoking.

Figure 1a represents the odds of smoking over time for an individual with all risk factors in the model equal to zero (ie, a non-Hispanic White male randomized to usual care who is not around or interacting with smokers, has no smoking restrictions or cigarettes available, has not consumed alcohol in the last hour with average urge to smoke, restlessness, negative affect, positive affect, positive coping expectancies, commitment to being smoke free, self-efficacy to abstain from smoking, HSI, and years of education completed).

Figure 1b shows that the odds of smoking gradually decreased over the assessment period for a typical individual randomized to CM, compared with individuals randomized to usual care. This relation became significant 4 days before the quit attempt and remained significant throughout the assessment period.

For a typical individual, urge to smoke had an inconsistent relation with smoking over time (Figure 1c). While predominately a positive and overall increasing relation, it was borderline significant during the 3 days before the scheduled quit attempt and again 6 days after the quit attempt. For interpretation, a 1 SD increase in urge to smoke around the quit date was associated with about 1.2 times greater odds of smoking for a typical individual, holding all else constant. By the end of the assessment period, the odds of smoking had increased to about 1.5 times greater.

In Figure 1d, we see that restlessness had a negative, relation with smoking over time that briefly became nonsignificant the day after the quit date. Figure 1, e and f represent the relation between negative affect (ie, feelings of irritability, frustration/anger, sadness, worry, misery) and positive affect (ie, feelings of happiness, calmness) with smoking over time, respectively. Positive affect had a linear, negative relation with smoking that diminished after the quit attempt. Negative affect showed a positive relation with smoking after the quit attempt that increased abruptly on day 0 and leveled off soon after.

We observed a relatively constant, negative relation between positive coping expectancies and smoking over time, which became significant 2–3 days before the quit attempt and remained significant through the fifth day postquit (Figure 1g). We also observed a fluctuating, negative relation between commitment to being smoke free and smoking over time. Early in the assessment period, we found that a 1 SD increase in commitment to being smoke free was associated with a 25% decrease in the odds of smoking for the typical individual, holding all else constant (Figure 1h). However, this association was only significant briefly prequit.

For the entire assessment period, self-efficacy to abstain from smoking had a negative relation with smoking over time (Figure 1i). The relation weakened slightly over the assessment period but remained significant until the last day of the assessment period. Also for the entire assessment period, having cigarettes available demonstrated an oscillating, positive relation with smoking (Figure 1j). By the second day after the quit attempt, a 1 SD increase in cigarette availability was associated with 1.5 times greater odds of smoking for the typical individual, holding all else constant (Figure 1j).

While the relation between having other smokers around and smoking increased soon after the quit attempt and remained positive thereafter, it never became statistically significant (Figure 1k). Being in an area where smoking was discouraged was not associated with lower odds of smoking over time compared with being where smoking was allowed (Figure 1l). However, being in an area where smoking was forbidden showed a decreasing, negative difference in the odds of smoking over time compared with being where smoking was allowed (Figure 1m). This relation became significant 1 day before the start of the quit attempt and remained significant through the end of the assessment period.

Consuming alcohol in the past hour was associated with an increasing, positive relation with smoking over time compared with no consumption, which was only significant in the last 3 days of the assessment period (Figure 1n). Additionally, interacting with smokers showed no statistically significant relation with smoking over time (Figure 1o).

Discussion

In this study, we used a logistic TVEM to analyze the relation between smoking and risk factors around a quit attempt in a sample of socioeconomically disadvantaged smokers who were interested in quitting. These relations are commonly found to be inconsistent over time,5,20 exemplifying the complexity of smoking behaviors and illuminating the need to improve understanding of the link between smoking and known risk factors.

We found that CM was effective before the scheduled quit attempt and became increasingly effective through the first week after the quit attempt. Recall that individuals were informed of their treatment assignment at baseline, and those in the CM group received financial incentives for confirmed 12-h abstinence on the scheduled quit date. Thus, it is reasonable that the treatment would have an effect on smoking behaviors before the quit attempt. Kendzor et al.25 showed that CM remained effective for reducing lapse through 12 weeks postquit. The current results suggest that the use of CM may not only be effective soon after a planned quit attempt, like pharmacological treatments,10 but may also encourage individuals to reduce smoking before the quit attempt as well.

In this analysis, we were also able to reproduce two previously observed, time-varying relations with lapse, urge to smoke and negative affect.5,10,29 Similar to a previous study,10 we found a convex relation between urge to smoke and smoking just after the quit attempt and a positive relation between negative affect and smoking during the first week after the quit attempt. Additionally, our analysis revealed time-varying relations between smoking and restlessness, positive affect, positive coping expectancies, commitment to being smoke free, self-efficacy for quitting, having cigarettes available, smoking restrictions, and alcohol consumption. To our knowledge, this is the first time these time-varying relations have been identified. These results support the hypothesis that there are situational antecedents to smoking.20 Further, findings suggest that relations between environmental, behavioral, cognitive, and affective risk factors and smoking vary over time. Future studies will be needed to replicate these results with other populations.

There are several limitations to this study. Although participants were followed for 12 weeks, EMAs were only collected for 1 week before and 1 week after the scheduled quit attempt. Thus, this work is limited to the temporal relation between risk factors and smoking only during the early phase of a quit attempt. Further, smartphone-based self-reports of smoking status were not biochemically verified, and studies have shown that individuals are not always honest in their reports about smoking.30 In addition, the majority of the study sample (71.8%) had a total household income less than $20000 a year, and participants were required to smoke 5 or more cigarettes per day to be included in the study. Thus, it is unclear if results would generalize to smokers with higher SES or lower levels of daily smoking. Finally, it is possible that the frequency of EMAs may have caused changes in participant experiences or behavior. For example, McCarthy et al.31 found that EMA frequency was related to lower cravings, anxiety, anger, and positive affect, but not smoking lapse. However, multiple EMA studies examining substance use disorders have found that EMA methods generally result in little to no reactivity.32–34

The method used in our analysis is able to deliver distinct and useful perspectives on smoking in the weeks before and after a quit attempt, but it requires large data sets and thorough understanding of the method, in order for it to be applied appropriately. Further developments for this method, including variable selection methods for TVEMs, would provide researchers a more robust toolset for the complex data structures found in ILD.

Ecological momentary interventions, which use mobile technology to deliver real-time, health behavior interventions to individuals in their natural environments, can be tailored to respond to individuals’ current internal and external cues to unhealthy behaviors by using information collected with EMAs.21,35,36 In previous smoking cessation studies, researchers have successfully used mobile technology-delivered resources (eg, advice, instructions, guided meditation, motivational support, distractions) to discourage smoking behaviors. Some of these studies have been tailored to individuals’ baseline characteristics (eg, age, nicotine dependence, gender)37,38 and have allowed individuals to request personalized resources based on their current needs (eg, experiencing an urge to smoke or recently lapsing).38,39 However, it is important to intervene further at critical times given an individual’s current risk for smoking.40 Behavioral scientists could use the temporal relations between smoking and risk factors identified in this study to aid in the design of ecological momentary interventions that provide specific resources that are tailored to individuals at the most opportune times given their current cognitions, affect, behaviors, and environments. This type of novel real-time intervention has only recently become possible due to the recent growth in smartphone ownership in general (ie, 77% of Americans own smartphones) and among low-income adults (ie, 64% own smartphones).41

Supplementary Material

Supplementary data are available at Nicotine and Tobacco Research online.

Funding

This work was supported by the Predoctoral Fellowship, University of Texas School of Public Health Cancer Education and Career Development Program—National Cancer Institute/NIH (R25 CA57712 to MDK); the University of Texas Health Science Center at Houston School of Public Health, Training Program in Biostatistics National Institute of General Medical Sciences at the National Institutes of Health Predoctoral Traineeship (T32GM074902 to MDK); the Michael & Susan Dell Foundation, Michael & Susan Dell Center for Healthy Living, The University of Texas School of Public Health, Austin Regional Campus; the University of Texas Health Science Center, School of Public Health; and the American Cancer Society (MRSGT-10-104-01-CPHPS to DEK and MRSGT-12-114-01-CPPB to MSB). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Declaration of Interests

None declared.

Supplementary Material

Supplementary_Information
Click here for additional data file. (31KB, docx)

References

  • 1. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291(10):1238–1245. [DOI] [PubMed] [Google Scholar]
  • 2. Centers for Disease Control and Prevention. Cigarette Smoking among Adults—United States, 2000. 2002:0149–2195.
  • 3. Piasecki TM, Fiore MC, McCarthy DE, Baker TB. Have we lost our way? The need for dynamic formulations of smoking relapse proneness. Addiction. 2002;97(9):1093–1108. [DOI] [PubMed] [Google Scholar]
  • 4. Kassel JD, Shiffman S. What can hunger teach us about drug craving? A comparative analysis of the two constructs. Behav Res Ther. 1992;14(3):141–167. [Google Scholar]
  • 5. Wray JM, Gass JC, Tiffany ST. A systematic review of the relationships between craving and smoking cessation. Nicotine Tob Res. 2013;15(7):1167–1182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Piasecki TM. Relapse to smoking. Clin Psychol Rev. 2006;26(2):196–215. [DOI] [PubMed] [Google Scholar]
  • 7. Lanza ST, Vasilenko SA, Liu X, Li R, Piper ME. Advancing the understanding of craving during smoking cessation attempts: a demonstration of the time-varying effect model. Nicotine Tob Res. 2013;16(suppl 2):S127–S134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Mason M, Mennis J, Way T, Lanza S, Russell M, Zaharakis N. Time-varying effects of a text-based smoking cessation intervention for urban adolescents. Drug Alcohol Depend. 2015;157:99–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Shiyko MP, Lanza ST, Tan X, Li R, Shiffman S. Using the time-varying effect model (TVEM) to examine dynamic associations between negative affect and self confidence on smoking urges: differences between successful quitters and relapsers. Prev Sci. 2012;13(3):288–299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Vasilenko SA, Piper ME, Lanza ST, Liu X, Yang J, Li R. Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine Tob Res. 2014;16(suppl 2):S135–S143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Dziak JJ, Li R, Tan X, Shiffman S, Shiyko MP. Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects. Psychol Methods. 2015;20(4):444–469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Koslovsky MD, Swartz MD, Leon-Novelo L et al. Deterministic Bayesian variable selection for multistate models, with applications to ecological momentary assessment of an attempt to quit smoking. Electronic Poster Presentation presented at Joint Statistical Meetings; 2016; Chicago, IL. [Google Scholar]
  • 13. Shiffman S, Hufford M, Hickcox M, Paty JA, Gnys M, Kassel JD. Remember that? A comparison of real-time versus retrospective recall of smoking lapses. J Consult Clin Psychol. 1997;65(2):292–300. [DOI] [PubMed] [Google Scholar]
  • 14. Walls TA, Schafer JL.. Models for intensive longitudinal data. Oxford University Press; 2006. [Google Scholar]
  • 15. Shiyko M, Naab P, Shiffman S, Li R. Modeling complexity of EMA data: time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine Tob Res. 2014;16(suppl 2):S144–S150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Zhou X, Nonnemaker J, Sherrill B, Gilsenan AW, Coste F, West R. Attempts to quit smoking and relapse: factors associated with success or failure from the ATTEMPT cohort study. Addict Behav. 2009;34(4):365–373. [DOI] [PubMed] [Google Scholar]
  • 17. Shiffman S, Kirchner TR, Ferguson SG, Scharf DM. Patterns of intermittent smoking: an analysis using Ecological Momentary Assessment. Addict Behav. 2009;34(6–7):514–519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Shiffman S, Paty JA, Gnys M, Kassel JA, Hickcox M. First lapses to smoking: within-subjects analysis of real-time reports. J Consult Clin Psychol. 1996;64(2):366–379. [DOI] [PubMed] [Google Scholar]
  • 19. Cohen LM, McCarthy DM, Brown SA, Myers MG. Negative affect combines with smoking outcome expectancies to predict smoking behavior over time. Psychol Addict Behav. 2002;16(2):91–97. [DOI] [PubMed] [Google Scholar]
  • 20. Shiffman S, Gwaltney CJ, Balabanis MH et al. Immediate antecedents of cigarette smoking: an analysis from ecological momentary assessment. J Abnorm Psychol. 2002;111(4):531–545. [DOI] [PubMed] [Google Scholar]
  • 21. Businelle MS, Ma P, Kendzor DE, Frank S, Wetter DW, Vidrine DJ. Using intensive longitudinal data collected via mobile phone to detect imminent lapse in smokers undergoing a scheduled quit attempt. J Med Internet Res. 2016;18:e275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Businelle MS, Ma P, Kendzor DE et al. Predicting quit attempts among homeless smokers seeking cessation treatment: an ecological momentary assessment study. Nicotine Tob Res. 2014;16(10):1371–1378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Gwaltney CJ, Shiffman S, Norman GJ et al. Does smoking abstinence self-efficacy vary across situations? Identifying context-specificity within the Relapse Situation Efficacy Questionnaire. J Consult Clin Psychol. 2001;69(3):516–527. [PubMed] [Google Scholar]
  • 24. Gwaltney CJ, Shiffman S, Sayette MA. Situational correlates of abstinence self-efficacy. J Abnorm Psychol. 2005;114(4):649–660. [DOI] [PubMed] [Google Scholar]
  • 25. Kendzor DE, Businelle MS, Poonawalla IB et al. Financial incentives for abstinence among socioeconomically disadvantaged individuals in smoking cessation treatment. Am J Public Health. 2015;105(6):1198–1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Wood SN. Generalized additive models: an introduction with R. Boca Raton, FL: CRC press; 2017. [Google Scholar]
  • 27. Tan X, Shiyko MP, Li R, Li Y, Dierker L. A time-varying effect model for intensive longitudinal data. Psychol Methods. 2012;17(1):61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Selected Papers of Hirotugu Akaike New York: Springer; 1998:199–213.
  • 29. Shiffman S, Waters AJ. Negative affect and smoking lapses: a prospective analysis. J Consult Clin Psychol. 2004;72(2):192–201. [DOI] [PubMed] [Google Scholar]
  • 30. Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob Res. 2009;11(1):12–24. [DOI] [PubMed] [Google Scholar]
  • 31. McCarthy DE, Minami H, Yeh VM, Bold KW. An experimental investigation of reactivity to ecological momentary assessment frequency among adults trying to quit smoking. Addiction. 2015;110(10):1549–1560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Hufford MR, Shields AL, Shiffman S, Paty J, Balabanis M. Reactivity to ecological momentary assessment: an example using undergraduate problem drinkers. Psychol Addict Behav. 2002;16(3):205–211. [PubMed] [Google Scholar]
  • 33. Litt MD, Cooney NL, Morse P. Ecological momentary assessment (EMA) with treated alcoholics: methodological problems and potential solutions. Health Psychol. 1998;17(1):48–52. [DOI] [PubMed] [Google Scholar]
  • 34. Simpson TL, Kivlahan DR, Bush KR, McFall ME. Telephone self-monitoring among alcohol use disorder patients in early recovery: a randomized study of feasibility and measurement reactivity. Drug Alcohol Depend. 2005;79(2):241–250. [DOI] [PubMed] [Google Scholar]
  • 35. Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(Pt 1):1–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Businelle MS, Ma P, Kendzor DE, Frank SG, Vidrine DJ, Wetter DW. An ecological momentary intervention for smoking cessation: evaluation of feasibility and effectiveness. J Med Internet Res. 2016;18(12):e321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Balmford J, Borland R. How do smokers use a smoking cessation text messaging intervention?Nicotine Tob Res. 2014;16(12):1586–1592. [DOI] [PubMed] [Google Scholar]
  • 38. Free C, Knight R, Robertson S et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. 2011;378(9785):49–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Rodgers A, Corbett T, Bramley D et al. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tob Control. 2005;14(4):255–261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Borland R, Balmford J, Hunt D. The effectiveness of personally tailored computer-generated advice letters for smoking cessation. Addiction. 2004;99(3):369–377. [DOI] [PubMed] [Google Scholar]
  • 41. Smith A. Record Shares of Americans Now Own Smartphones, have Home Broadband. Pew Internet & American Life Project; 2017; http://www.pewresearch.org/fact-tank/2017/01/12/evolution-of-technology/. Accessed July 3, 2017. [Google Scholar]

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Supplementary Materials

Supplementary_Information
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