scholarly journals 0186 The Relationships between General Technology Use, Technology Use at Bedtime, and Sleep

SLEEP ◽  
2019 ◽  
Vol 42 (Supplement_1) ◽  
pp. A76-A76
Author(s):  
Jennifer Peszka ◽  
Marc A Sestir ◽  
Lindsay A Kennedy ◽  
David F Mastin
2020 ◽  
Author(s):  
Uchenna Nwokeji ◽  
Erin M. Spaulding ◽  
Rongzi Shan ◽  
Ruth-Alma Turkson-Ocran ◽  
Diana Baptiste ◽  
...  

BACKGROUND Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality worldwide. Health-Information Technologies (HIT) have recently emerged as a viable intervention to mitigate the burden of ASCVD. At least 60% of United States (US) adults report searching the internet for health information; however, previous research has not examined the prevalence of general technology or HIT use between adults with and without ASCVD. In addition, social determinants in HIT use among adults with ASCVD are not well understood. OBJECTIVE To evaluate the prevalence and social determinants of HIT use among U.S. adults with vs without self-reported ASCVD. METHODS We pooled cross-sectional data from the 2011-2018 National Health Interview Survey (NHIS) to examine general technology and HIT use between adults aged ≥18 years with and without self-reported ASCVD (coronary heart disease and/or stroke). General technology use was defined as mobile phone ownership, Internet use, and computer use. HIT use was defined as looking up health information on the Internet, filling a prescription online, scheduling a medical appointment on the Internet, communicating with a healthcare provider by email, or using online group chats to learn about health topics. We evaluated sociodemographic differences in HIT use among respondents using Poisson regression. Analyses were weighted according to NHIS standards. RESULTS A total sample of N=256,534 individuals were included, 2,194 (0.9%) reported prior ASCVD. Among adults with prior ASCVD, the mean (±SD) age was 70.6 (11.5) years, and 47% were female. General technology use differed between participants with and without prior ASCVD, with 36% (657/1,826) and 76% (162,500/213,816) indicating internet usage and 25% (394/1,575) and 61% (112,580/184,557) indicating using a computer every day, respectively. Similarly, adults with ASCVD were less likely to use HIT use than those without ASCVD (25% vs. 51%, p<0.001). Among adults with prior ASCVD, social determinants that were associated with HIT use included younger age, higher education, higher income, being employed, and being married. CONCLUSIONS HIT use was low among adults with a history of ASCVD, which may represent a barrier to delivering care via emerging HIT. Given the associations with social determinants such as income, education and employment, targeted strategies and policies are needed to eliminate barriers to impact HIT usage. CLINICALTRIAL N/A


2021 ◽  
Author(s):  
Victoria R. Nelson ◽  
Katharine M. Mitchell ◽  
Bree E Holtz

Abstract Purpose: In this paper, we explore how health technology use impacts informal caregivers’ health and how sociodemographic factors are related, using the Health Information National Trends Survey (HINTS). Methods: Data for this study were obtained from the National Cancer Institutes’ Health Information and National Trends Survey (HINTS 5, Cycle 2, 2018). Participants for the current study were chosen based on their response to one question related to their caregiving status. The sample size was 483 respondents. Variables of interest included caregiver relationship type, general technology use, portal use, and overall health status. Results: The results indicate that there was not a significant difference of caregiving role on portal usage, [F(5,99) = .975, p = .44, η2 = .049], and technology use [F(7, 462)=2.625, p=.01]. This demonstrates that those caregiving for a child are more likely to use technology for health related issues. There was not a significant effect of portal use on caregiver health. However, there was a significant effect of technology use on overall health (t = 2.074, p=.04). There was also a significant effect of demographics on general technology use [F(7, 434)= 14.858, p < .001]. Demonstrating as education and income increases, technology use also increases, and as age increases technology use decreases. Conclusion: This study affirmed that demographic inequalities can negatively impact technology and portal use, which could reduce the burden on caregivers. Therefore, it is important to work to engage cancer patients and their caregivers with technological support and resources.


OTO Open ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 2473974X2110186
Author(s):  
Chloe B. Warinner ◽  
Tuna C. Hayirli ◽  
Regan W. Bergmark ◽  
Rosh Sethi ◽  
Eleni M. Rettig

Objective To describe baseline technology use within the head and neck cancer (HNC) population prior to the COVID-19 pandemic. Study Design Cross-sectional analysis of National Health Interview Survey (NHIS) data. Setting The NHIS is a survey of population health administered in person annually to a nationally representative sample of noninstitutionalized US residents via a complex clustered sampling design. Methods Data regarding technology use, cancer history, and demographics were extracted from the NHIS. The study population comprised individuals who completed the NHIS Sample Adult survey from 2012 to 2018 and self-reported a cancer diagnosis. Poisson regression was used to evaluate associations between demographics and general or health-related technology use and prevalence ratios reported. Results Patients with HNC were less likely to use general technology (computers, internet, or email) when compared with other patients with cancer (60% vs 73%, P < .001), although this difference was not statistically significant after controlling for sociodemographic factors. Among patients with HNC, older age, lower education, and lower income were negatively associated with general technology use (adjusted prevalence ratio [aPR], 0.71 [95% CI, 0.59-0.87] for age 65-79 years vs <50 years; aPR, 0.66 [95% CI, 0.51-0.85] for high school vs master; aPR, 0.66 [95% CI, 0.48-0.91] for income 100%-200% vs >400% federal poverty level). Older age and lower education were negatively associated with health-related technology use (aPR, 0.46 [95% CI, 0.32-0.67] for age 65-79 years vs <50 years; aPR, 0.47 [95% CI, 0.30-0.74] for high school vs master). Conclusion Socioeconomic disparities exist in technology use rates among patients with HNC. Access to technology may pose a barrier to telehealth visits for many patients with HNC due to the unique socioeconomic demographics of this patient population.


1997 ◽  
Vol 6 (1-2) ◽  
pp. 63-75 ◽  
Author(s):  
William C. Mann

2013 ◽  
Author(s):  
Virginia Wadley ◽  
Rachel Benz ◽  
Martha Frankel ◽  
David Ball ◽  
Daniel Roenker

2018 ◽  
Vol 49 (3) ◽  
pp. 205-219 ◽  
Author(s):  
Robert L. Glueckauf ◽  
Marlene M. Maheu ◽  
Kenneth P. Drude ◽  
Brittny A. Wells ◽  
Yuxia Wang ◽  
...  

2020 ◽  
Author(s):  
Ananta Addala ◽  
Marie Auzanneau ◽  
Kellee Miller ◽  
Werner Maier ◽  
Nicole Foster ◽  
...  

<b>Objective:</b> As diabetes technology use in youth increases worldwide, inequalities in access may exacerbate disparities in hemoglobin A1c (HbA1c). We hypothesized an increasing gap in diabetes technology use by socioeconomic status (SES) would be associated with increased HbA1c disparities. <p> </p> <p><b>Research Design and Methods: </b>Participants aged <18 years with diabetes duration ≥1 year in the Type 1 Diabetes Exchange (T1DX, US, n=16,457) and Diabetes Prospective Follow-up (DPV, Germany, n=39,836) registries were categorized into lowest (Q1) to highest (Q5) SES quintiles. Multiple regression analyses compared the relationship of SES quintiles with diabetes technology use and HbA1c from 2010-2012 and 2016-2018. </p> <p> </p> <p><b>Results: </b>HbA1c was higher in participants with lower SES (in 2010-2012 & 2016-2018, respectively: 8.0% & 7.8% in Q1 and 7.6% & 7.5% in Q5 for DPV; and 9.0% & 9.3% in Q1 and 7.8% & 8.0% in Q5 for T1DX). For DPV, the association between SES and HbA1c did not change between the two time periods, whereas for T1DX, disparities in HbA1c by SES increased significantly (p<0.001). After adjusting for technology use, results for DPV did not change whereas the increase in T1DX was no longer significant.</p> <p> </p> <p><b>Conclusions: </b>Although causal conclusions cannot be drawn, diabetes technology use is lowest and HbA1c is highest in those of the lowest SES quintile in the T1DX and this difference for HbA1c broadened in the last decade. Associations of SES with technology use and HbA1c were weaker in the DPV registry. </p>


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