Median number of cigarettes smoked per day among cigarette only users and dual users.

Dual use was associated with a slightly higher median number of cigarettes smoked per day (p < .0001). Error bars denote the interquartile range.</p

Medical symptoms and conditions by product use, N = 34,279.

Medical symptoms and conditions by product use, N = 34,279.</p

Percent of “yes” responses to past or current medical symptoms or conditions among cigarette only users and dual users.

<p>The only statistically significant difference between cigarette only users and dual users was arrhythmia (ADJ <i>p</i> = .02). Models adjusted for age, sex, race, education, cigarettes per day, and as needed, coronary artery disease, congestive heart failure, and COPD.</p

E-cigarette use dose measures among e-cigarette only users and dual users.

<p>(a) # days per month was defined as “number of days of e-cigarette use in the past 30 days” (b) # cartridges per day: “number of e-liquid cartridges/refills used per day” (c) # puffs per day: “number of puffs off an e-cigarette per day.” Error bars denote the interquartile range.</p

Median SF-36 general health scores, breathing difficulty “typically” scores, and breathing difficulty in the “past month” scores, among cigarette only users and dual users.

<p>Dual use was associated with lower (poorer) general health scores (ADJ <i>p</i> = .002) and higher (poorer) past month breathing difficulty scores (ADJ <i>p</i> = .001). Models adjusted for age, sex, race, education, cigarettes per day, coronary artery disease, congestive heart failure, and COPD. Error bars denote the interquartile range.</p

Prevalence of e-cigarette only, cigarette only, and dual use in the past 30 days by demographic characteristics and lifestyle and well-being factors in the health eheart study, N = 39,747^*.

<p>Prevalence of e-cigarette only, cigarette only, and dual use in the past 30 days by demographic characteristics and lifestyle and well-being factors in the health eheart study, N = 39,747^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198681#t001fn001" target="_blank">*</a>}.</p

Summary of single nucleotide polymorphisms analyzed for pro- and anti-inflammatory cytokine genes and the growth mixture model analysis for general sleep disturbance scale total score.

<p>Abbreviations: A  =  Additive model, Chr  =  chromosome, D  =  Dominant model, Hap  =  haplotype, IFNG  =  interferon gamma, IL  =  interleukin, MAF – minor allele frequency, n/a  =  not applicable, NFKB  =  nuclear factor kappa beta, R  =  Recessive model, SNP  =  single nucleotide polymorphism, TNFA  =  tumor necrosis factor alpha.</p

Multiple Logistic Regression Analyses for Interleukin 6 (IL6) rs35610689 and Nuclear Factor Kappa Beta 2 Subunit (NFKB2) rs7897947 to Predict Higher Sleep Disturbance Class.

<p>For each model, the first three principle components identified from the analysis of ancestry informative markers as well as self-report race/ethnicity (White, Asian/Pacific Islander, Black, Hispanic/Mixed background/Other) were retained in all models to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in the model included genotype (IL6 rs35610689: AA versus AG+GG; NFKB2 rs7897947: TT versus TG + GG), age (5 year increments), and functional status (KPS score, 10 point increments). Patient versus family caregiver (FC) status could not be included in the regression analyses because no FCs were included in the higher sleep disturbance class.</p

GMM parameter estimates for general sleep disturbance scale latent class^a solution with 7 assessments, with dyad as a clustering variable.

*<p>p<.05, **p<.01, ^***p<.001.</p>a<p>Trajectory group sizes are for classification of individuals based on their most likely latent class probabilities.</p>b<p>Growth mixture model estimates were obtained with robust maximum likelihood, with dyad as a clustering variable to account for dependency between patients and caregivers within the same dyad. Quadratic slope variances were fixed at zero to improve estimation.</p><p>Abbreviations: GMM  =  Growth mixture model; S.E.  =  standard error.</p

Observed and estimated General Sleep Disturbance Scale (GSDS) trajectories for participants in each of the latent classes, as well as the mean GSDS scores for the total sample.

<p>Observed and estimated General Sleep Disturbance Scale (GSDS) trajectories for participants in each of the latent classes, as well as the mean GSDS scores for the total sample.</p