scholarly journals Electronic Health Literacy Among Magnetic Resonance Imaging and Computed Tomography Medical Imaging Outpatients: Cluster Analysis

10.2196/13423 ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. e13423 ◽  
Author(s):  
Lisa Lynne Hyde ◽  
Allison W Boyes ◽  
Lisa J Mackenzie ◽  
Lucy Leigh ◽  
Christopher Oldmeadow ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Medical Imaging ◽  
Health Information ◽  
Odds Ratio ◽  
Latent Class ◽  
Measurement Tool ◽  
Class Membership ◽  
Cut Points ◽  
Ehealth Literacy ◽  
Electronic Health

Background Variations in an individual’s electronic health (eHealth) literacy may influence the degree to which health consumers can benefit from eHealth. The eHealth Literacy Scale (eHEALS) is a common measure of eHealth literacy. However, the lack of guidelines for the standardized interpretation of eHEALS scores limits its research and clinical utility. Cut points are often arbitrarily applied at the eHEALS item or global level, which assumes a dichotomy of high and low eHealth literacy. This approach disregards scale constructs and results in inaccurate and inconsistent conclusions. Cluster analysis is an exploratory technique, which can be used to overcome these issues, by identifying classes of patients reporting similar eHealth literacy without imposing data cut points. Objective The aim of this cross-sectional study was to identify classes of patients reporting similar eHealth literacy and assess characteristics associated with class membership. Methods Medical imaging outpatients were recruited consecutively in the waiting room of one major public hospital in New South Wales, Australia. Participants completed a self-report questionnaire assessing their sociodemographic characteristics and eHealth literacy, using the eHEALS. Latent class analysis was used to explore eHealth literacy clusters identified by a distance-based cluster analysis, and to identify characteristics associated with class membership. Results Of the 268 eligible and consenting participants, 256 (95.5%) completed the eHEALS. Consistent with distance-based findings, 4 latent classes were identified, which were labeled as low (21.1%, 54/256), moderate (26.2%, 67/256), high (32.8%, 84/256), and very high (19.9%, 51/256) eHealth literacy. Compared with the low class, participants who preferred to receive a lot of health information reported significantly higher odds of moderate eHealth literacy (odds ratio 16.67, 95% CI 1.67-100.00; P=.02), and those who used the internet at least daily reported significantly higher odds of high eHealth literacy (odds ratio 4.76, 95% CI 1.59-14.29; P=.007). Conclusions The identification of multiple classes of eHealth literacy, using both distance-based and latent class analyses, highlights the limitations of using the eHEALS global score as a dichotomous measurement tool. The findings suggest that eHealth literacy support needs vary in this population. The identification of low and moderate eHealth literacy classes indicate that the design of eHealth resources should be tailored to patients’ varying levels of eHealth literacy. eHealth literacy improvement interventions are needed, and these should be targeted based on individuals’ internet use frequency and health information amount preferences.

scholarly journals Electronic Health Literacy Among Magnetic Resonance Imaging and Computed Tomography Medical Imaging Outpatients: Cluster Analysis (Preprint)

2019 ◽  
Author(s):  
Lisa Lynne Hyde ◽  
Allison W Boyes ◽  
Lisa J Mackenzie ◽  
Lucy Leigh ◽  
Christopher Oldmeadow ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Medical Imaging ◽  
Health Information ◽  
Odds Ratio ◽  
Latent Class ◽  
Self Report ◽  
Measurement Tool ◽  
Cut Points ◽  
Ehealth Literacy ◽  
Electronic Health

BACKGROUND Variations in an individual’s electronic health (eHealth) literacy may influence the degree to which health consumers can benefit from eHealth. The eHealth Literacy Scale (eHEALS) is a common measure of eHealth literacy. However, the lack of guidelines for the standardized interpretation of eHEALS scores limits its research and clinical utility. Cut points are often arbitrarily applied to the eHEALS item or at the global level, which assumes a dichotomy of high and low eHealth literacy. This approach disregards scale constructs and results in inaccurate and inconsistent conclusions. Cluster analysis is an exploratory technique, which can be used to overcome these issues, by identifying classes of patients reporting similar eHealth literacy without imposing data cut points. OBJECTIVE The aim of this cross-sectional study was to identify classes of patients reporting similar eHealth literacy and assess characteristics associated within each class. METHODS Medical imaging outpatients were recruited consecutively in the waiting room of one major public hospital in New South Wales, Australia. Participants completed a self-report questionnaire assessing their sociodemographic characteristics and eHealth literacy, using the eHEALS. Latent class analysis was used to explore eHealth literacy clusters identified by a distance-based cluster analysis, and to identify characteristics associated with class membership. RESULTS Of the 268 eligible and consenting participants, 256 (95.5%) completed the eHEALS. Consistent with distance-based findings, 4 latent classes were identified, which were labeled as low (21.1%, 54/256), moderate (26.2%, 67/256), high (32.8%, 84/256), and very high (19.9%, 51/256) eHealth literacy. Compared with the low class, participants who preferred to receive a lot of health information reported significantly higher odds of moderate eHealth literacy (odds ratio 16.67, 95% CI 1.67-100.00; P=.02), and those who used the internet at least daily reported significantly higher odds of high eHealth literacy (odds ratio 4.76, 95% CI 1.59-14.29; P=.007). CONCLUSIONS The identification of multiple classes of eHealth literacy, using both distance-based and latent class analyses, highlights the limitations of using the eHEALS global score as a dichotomous measurement tool. The findings suggest that eHealth literacy support needs vary in this population. The identification of low and moderate eHealth literacy classes indicate that the design of eHealth resources should be tailored to patients’ varying levels of eHealth literacy. eHealth literacy improvement interventions are needed, and these should be targeted based on individuals’ internet use frequency and health information amount preferences.

scholarly journals The Measurements and an Elaborated Understanding of Chinese eHealth Literacy (C-eHEALS) in Chronic Patients in China

2018 ◽  
Vol 15 (7) ◽  
pp. 1553 ◽  
Author(s):  
Angela Chang ◽  
Peter Schulz
Keyword(s):  
Health Literacy ◽  
Health Information ◽  
Health Care Providers ◽  
Computer Literacy ◽  
Strong Association ◽  
Care Providers ◽  
Chronic Patients ◽  
Online Questionnaire ◽  
Ehealth Literacy ◽  
Electronic Health

The rapid rise of Internet-based technologies to disseminate health information and services has been shown to enhance online health information acquisition. A Chinese version of the electronic health literacy scale (C-eHEALS) was developed to measure patients’ combined knowledge and perceived skills at finding and applying electronic health information to health problems. A valid sample of 352 interviewees responded to the online questionnaire, and their responses were analyzed. The C-eHEALS, by showing high internal consistency and predictive validity, is an effective screening tool for detecting levels of health literacy in clinical settings. Individuals’ sociodemographic status, perceived health status, and level of health literacy were identified for describing technology users’ characteristics. A strong association between eHealth literacy level, media information use, and computer literacy was found. The emphasis of face-to-face inquiry for obtaining health information was important in the low eHealth literacy group while Internet-based technologies crucially affected decision-making skills in the high eHealth literacy group. This information is timely because it implies that health care providers can use the C-eHEALS to screen eHealth literacy skills and empower patients with chronic diseases with online resources.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study (Preprint)

2020 ◽  
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

BACKGROUND eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. OBJECTIVE The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. METHODS Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. RESULTS Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. CONCLUSIONS Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

Electronic Health Literacy among Patients with Dental Diseases

2018 ◽  
Author(s):  
Saeideh Valizadeh-Haghi ◽  
Shahabedin Rahmatizadeh
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Information Quality ◽  
Educational Background ◽  
General Interest ◽  
P Value ◽  
Ehealth Literacy ◽  
Dental Patients ◽  
Dental Diseases ◽  
Electronic Health

To explore eHealth literacy and general interest in using eHealth information among patients with dental diseases. A total of 171 patients with dental diseases completed the survey including the eHEALS. The effect of participants' age, gender and education on eHealth literacy was assessed. Spearman’s correlation coefficient was also used to assess the correlation between the importance of access to health information and the usefulness of the internet for decision-making. The mean score of eHealth literacy in the participants was 30.55 (SD = 4.069) which shows that the participants had a high level of eHealth literacy. The participants' age has significant effect on eHealth literacy level (t = 3.573, P-value = 0.002). Moreover, there was a significant correlation between the total score of eHealth literacy and the importance of access to eHealth information (r = 0.33, n = 171, P < 0.001). The difference in eHealth literacy in terms of educational background showed no statistically significant differences (F = 1.179, P-value = 0.322). Determining eHealth literacy among dental patients leads to a better understanding of their problems in health decision-making. Furthermore, Dental institutions efforts should aim to raise awareness on online health information quality and to encourage patients to use evaluation tools, especially among low electronic health literate patients.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study

JMIR Cancer ◽  
10.2196/24137 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e24137
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

Background eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. Objective The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. Methods Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. Results Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. Conclusions Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

Safe Use of Electronic Health Records and Health Information Technology Systems

2013 ◽  
Vol 9 (4) ◽  
pp. 177-189 ◽  
Author(s):  
Charles R. Denham ◽  
David C. Classen ◽  
Stephen J. Swenson ◽  
Michael J. Henderson ◽  
Thomas Zeltner ◽  
...  
Keyword(s):  
Information Technology ◽  
Electronic Health Records ◽  
Health Information Technology ◽  
Health Information ◽  
Health Records ◽  
Electronic Health ◽  
Technology Systems

Stepfamily Involvement in and out of School and (Step)children’s College Preparation Behaviors: A Latent Cluster Analysis

2021 ◽  
pp. 0192513X2199387
Author(s):  
Jacqueline Bible ◽  
David T. Lardier ◽  
Frank Perrone ◽  
Brad van Eeden-Moorefield
Keyword(s):  
High School ◽  
Cluster Analysis ◽  
Longitudinal Study ◽  
Latent Class Analysis ◽  
College Preparation ◽  
Latent Class ◽  
Educational Experiences ◽  
Class Analysis ◽  
Out Of School

Using a latent class analysis (LCA) with data from a subsample of children in stepfamilies ( N = 6,637) from the 2009 High School Longitudinal Study (HSLS), this study examined how stepfamily involvement in their (step)child’s education in and outside of school influenced their (step)child’s college preparation. Stepfamily involvement in their (step)child’s education in school (e.g., help with homework) and outside of school (e.g., educational experiences such as going to a museum) may help overcome challenges associated with academic and college preparation for children in stepfamilies. Results broadly indicate students with higher stepfamily involvement in education in and out of school had (step)parents who believed that college was attainable, students engaged in more activities that would prepare them for their future, and students took more AP/IB level courses and tests. Together, findings suggest that stepfamily involvement in education both in and out of school is important for their (step)child’s college preparation behaviors.

Health Care Utilization, Transition Readiness, and Quality of Life: A Latent Class Analysis

2020 ◽  
Author(s):  
Katherine A Traino ◽  
Christina M Sharkey ◽  
Megan N Perez ◽  
Dana M Bakula ◽  
Caroline M Roberts ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Health Care Utilization ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Care Utilization ◽  
Class Membership ◽  
Transition Readiness ◽  
High Utilization

Abstract Objective To identify possible subgroups of health care utilization (HCU) patterns among adolescents and young adults (AYAs) with a chronic medical condition (CMC), and examine how these patterns relate to transition readiness and health-related quality of life (HRQoL). Methods Undergraduates (N = 359; Mage=19.51 years, SD = 1.31) with a self-reported CMC (e.g., asthma, allergies, irritable bowel syndrome) completed measures of demographics, HCU (e.g., presence of specialty or adult providers, recent medical visits), transition readiness, and mental HRQoL (MHC) and physical HRQoL (PHC). Latent class analysis identified four distinct patterns of HCU. The BCH procedure evaluated how these patterns related to transition readiness and HRQoL outcomes. Results Based on seven indicators of HCU, a four-class model was found to have optimal fit. Classes were termed High Utilization (n = 95), Adult Primary Care Physician (PCP)-Moderate Utilization (n = 107), Family PCP-Moderate Utilization (n = 81), and Low Utilization (n = 76). Age, family income, and illness controllability predicted class membership. Class membership predicted transition readiness and PHC, but not MHC. The High Utilization group reported the highest transition readiness and the lowest HRQoL, while the Low Utilization group reported the lowest transition readiness and highest HRQoL. Conclusions The present study characterizes the varying degrees to which AYAs with CMCs utilize health care. Our findings suggest poorer PHC may result in higher HCU, and that greater skills and health care engagement may not be sufficient for optimizing HRQoL. Future research should examine the High Utilization subgroup and their risk for poorer HRQoL.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

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