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

DataSheet1_Cholecystokinin-A signaling regulates automaticity of pacemaker cardiomyocytes.pdf

Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in isolated PCs.Methods and results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCKAR), we demonstrate expression of Cckar mRNA specifically in mouse PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCKAR. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCKAR, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCKAR. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling.Conclusion: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) Activation of the cholecystokinin-A signaling pathway regulates PC automaticity.</p

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

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

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

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

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

Baseline Characteristics by Average Screen-Time.

Baseline Characteristics by Average Screen-Time.</p

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep

<div><p>Background</p><p>Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality.</p><p>Aims</p><p>The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep.</p><p>Methods</p><p>We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index–PSQI) were obtained by survey. Linear regression was used to obtain effect estimates.</p><p>Results</p><p>Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency.</p><p>Conclusions</p><p>These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.</p></div

Geographical Distribution of Participants in the United States.

Abbreviations: AK, Alaska; HI, Hawaii. Dots represent the number of participants that resided in the zip-code corresponding to the placement on the map. All 50 states were represented and 147 (23%) resided in California. Created with Tableau Software (www.tableau.com) and published with permission of the company (S1 File). The U.S. map was used under a CC BY-SA copyright from OpenStreetMap contributors (www.openstreetmap.org/copyright).</p