scholarly journals The Relationship Between Technology Use and Eating Behavior

2021 ◽  
Author(s):  
ecem gökbike ersen
Keyword(s):  
Eating Behavior ◽  
Technology Use ◽  
The Relationship

A Decade of Disparities in Diabetes Technology Use and HbA1c in Pediatric Type 1 Diabetes: A Trans-Atlantic Comparison

2020 ◽  
Author(s):  
Ananta Addala ◽  
Marie Auzanneau ◽  
Kellee Miller ◽  
Werner Maier ◽  
Nicole Foster ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Technology Use ◽  
Hemoglobin A1c ◽  
Diabetes Technology ◽  
Design And Methods ◽  
Relationship Of ◽  
The Relationship ◽  
Pediatric Type 1 Diabetes

<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>

Eating Behavior of Brazilian College Students: Influences of Lifestyle, Negative Affectivity, and Personal Characteristics

2020 ◽  
pp. 003151252098308
Author(s):  
Bianca G. Martins ◽  
Wanderson R. da Silva ◽  
João Marôco ◽  
Juliana A. D. B. Campos
Keyword(s):  
College Students ◽  
Eating Behavior ◽  
Emotional Eating ◽  
Eating Behaviors ◽  
Structural Model ◽  
Negative Affectivity ◽  
Personal Characteristics ◽  
Second Order ◽  
The Impact ◽  
The Relationship

In this study we proposed to estimate the impact of lifestyle, negative affectivity, and college students’ personal characteristics on eating behavior. We aimed to verify that negative affectivity moderates the relationship between lifestyle and eating behavior. We assessed eating behaviors of cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE)) with the Three-Factor Eating Questionnaire-18. We assessed lifestyle with the Individual Lifestyle Profile, and we assessed negative affectivity with the Depression, Anxiety and Stress Scale-21. We constructed and tested (at p < .05) a hypothetical causal structural model that considered global (second-order) and specific (first-order) lifestyle components, negative affectivity and sample characteristics for each eating behavior dimension. Participants were 1,109 college students ( M age = 20.9, SD = 2.7 years; 65.7% females). We found significant impacts of lifestyle second-order components on negative affectivity (β = −0.57–0.19; p < 0.001–0.01) in all models. Physical and psychological lifestyle components impacted directly only on CR (β=−0.32–0.81; p < 0.001). Negative affectivity impacted UE and EE (β = 0.23–0.30; p < 0.001). For global models, we found no mediation pathways between lifestyle and CR or UE. For specific models, negative affectivity was a mediator between stress management and UE (β=−0.07; p < 0.001). Negative affectivity also mediated the relationship between thoughts of dropping an undergraduate course and UE and EE (β = 0.06–0.08; p < 0.001). Participant sex and weight impacted all eating behavior dimensions (β = 0.08–0.34; p < 0.001–0.01). Age was significant for UE and EE (β=−0,14– −0.09; p < 0.001–0.01). Economic stratum influenced only CR (β = 0.08; p = 0.01). In sum, participants’ lifestyle, negative emotions and personal characteristics were all relevant for eating behavior assessment.

scholarly journals Eating and body image: Does food insecurity make us feel thinner?

2017 ◽  
Vol 40 ◽  
Author(s):  
Klaudia B. Ambroziak ◽  
Elena Azañón ◽  
Matthew R. Longo
Keyword(s):  
Eating Disorders ◽  
Body Image ◽  
Food Insecurity ◽  
Eating Behavior ◽  
Healthy Individuals ◽  
Insurance Hypothesis ◽  
Central Aspect ◽  
Food Scarcity ◽  
Clinical Conditions ◽  
The Relationship

AbstractBody image distortions are common in healthy individuals and a central aspect of serious clinical conditions, such as eating disorders. This commentary explores the potential implications of body image and its distortions for the insurance hypothesis. In particular, we speculate that body image may be an intervening variable mediating the relationship between perceived food scarcity and eating behavior.

Combined Model of Technology Use and Medical Adherence in eHealth Technology Implementation

2021 ◽  
Vol 65 (1) ◽  
pp. 776-780
Author(s):  
Bijita Devkota ◽  
Fernando Montalvo ◽  
Daniel S. McConnell ◽  
Janan A. Smither
Keyword(s):  
Human Factors ◽  
Technology Acceptance ◽  
Treatment Adherence ◽  
Technology Use ◽  
Technology Implementation ◽  
Medical Information ◽  
Treatment Protocols ◽  
Lower Treatment ◽  
Relationship Of ◽  
The Relationship

eHealth applications are expected to improve the effectiveness and efficiency of healthcare systems by providing improved medical information flow between medical providers and patients. Although the technology is expected to empower patients, lower treatment costs, and provide real-time collection of health data, individuals may be apprehensive about the use and efficacy of eHealth technologies. Medical professionals are often unaware of human factors technology acceptance or usability models which impact the use of medically focused technology, such as eHealth applications. Similarly, human factors professionals are often unaware of treatment adherence models which map the relationship of illness factors and individual differences to treatment protocols. The present paper presents a theoretical approach through which technology acceptance and usability models should be combined with medical treatment adherence models to ensure that eHealth applications are used properly and effectively.

Communication technology use and study skills

2017 ◽  
Vol 18 (3) ◽  
pp. 257-270 ◽  
Author(s):  
Penny Thompson
Keyword(s):  
Online Social Networks ◽  
Technology Use ◽  
Study Skills ◽  
Text Messaging ◽  
Instant Messaging ◽  
Negative Relationship ◽  
Communication Technologies ◽  
Study Strategies ◽  
Learning And Study Strategies ◽  
The Relationship

Research suggests a negative relationship between frequent use of communication technologies, such as text messaging and social network sites, and academic performance, but the nature of the relationship needs to be explored in greater detail. This study explored the relationship between use of communication technologies and self-reported study skills. A total of 74 first-year university students completed the online Learning and Study Strategies Inventory and reported on how frequently they used text messaging, instant messaging, and online social networks such as Facebook. Correlation analysis indicated a negative relationship between frequency of communications technology use and the Learning and Study Strategies Inventory measure of Concentration. While the study does not prove a causal relationship, it provides more detail on the specific study skills challenges students may be facing when they interrupt their studying with frequent online social communication. This increased understanding can help educators tailor study skills interventions and support more directly to students’ needs.

scholarly journals Examining Rural and Racial Disparities in the Relationship Between Loneliness and Social Technology Use Among Older Adults

2021 ◽  
Vol 9 ◽  
Author(s):  
Kaileigh A. Byrne ◽  
Reza Ghaiumy Anaraky ◽  
Cheryl Dye ◽  
Lesley A. Ross ◽  
Kapil Chalil Madathil ◽  
...  
Keyword(s):  
Older Adults ◽  
Racial Disparities ◽  
Technology Use ◽  
Social Engagement ◽  
Social Contact ◽  
Primary Study ◽  
Social Technology ◽  
Study Results ◽  
The Relationship ◽  
Rural Older Adults

Loneliness, the subjective negative experience derived from a lack of meaningful companionship, is associated with heightened vulnerability to adverse health outcomes among older adults. Social technology affords an opportunity to cultivate social connectedness and mitigate loneliness. However, research examining potential inequalities in loneliness is limited. This study investigates racial and rural-urban differences in the relationship between social technology use and loneliness in adults aged 50 and older using data from the 2016 wave of the Health and Retirement Study (N = 4,315). Social technology use was operationalized as the self-reported frequency of communication through Skype, Facebook, or other social media with family and friends. Loneliness was assessed using the UCLA Loneliness scale, and rural-urban differences were based on Beale rural-urban continuum codes. Examinations of race focused on differences between Black/African-American and White/Caucasian groups. A path model analysis was performed to assess whether race and rurality moderated the relationship between social technology use and loneliness, adjusting for living arrangements, age, general computer usage. Social engagement and frequency of social contact with family and friends were included as mediators. The primary study results demonstrated that the association between social technology use and loneliness differed by rurality, but not race. Rural older adults who use social technology less frequently experience greater loneliness than urban older adults. This relationship between social technology and loneliness was mediated by social engagement and frequency of social contact. Furthermore, racial and rural-urban differences in social technology use demonstrated that social technology use is less prevalent among rural older adults than urban and suburban-dwelling older adults; no such racial differences were observed. However, Black older adults report greater levels of perceived social negativity in their relationships compared to White older adults. Interventions seeking to address loneliness using social technology should consider rural and racial disparities.

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