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 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 Web-based health Information Seeking and eHealth Literacy among College students. A Self-report study

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Fatemeh KHademian ◽  
Mahsa Roozrokh Arshadi Montazer ◽  
Azam Aslani
Keyword(s):  
College Students ◽  
Health Information ◽  
Information Seeking ◽  
Health Resources ◽  
Self Report ◽  
The Internet ◽  
Health Information Seeking ◽  
Web Based ◽  
Ehealth Literacy ◽  
Quality Health

Objective. This study aimed to assess web-based health information seeking and eHealth literacy among Iranian college students. Methods. The study was conducted in five colleges of the Shiraz University of Medical Sciences in Iran during 2018. The data were collected by a researcher-made questionnaire consisting of seven questions on a 4-point Likert-type scale, with scores ranging from 7 to 28. These questions were: ′I know how to use the Internet to answer my questions about health′, ′I think there is enough information about health-related issues on the Internet′, ′I know the vocabulary used in health issues on the Internet′, ′I can tell high-quality health resources from low-quality health resources on the Internet′, ′I know how to use the health information I find on the Internet to help me′, ′I feel confident in using information from the Internet to make health decisions′, and ′Searching for health-related information on the Internet will increase my knowledge in this field′. High eHealth literacy level is defined as above the total mean score and low eHealth literacy level is defined as lower than the total mean score. Results. In all, 386 college students participated in the study. The results showed that the mean score of eHealth literacy was 19.11 out of 28; 205 participants (54.4%) had low eHealth literacy. In addition, the students used the Internet to search for information regarding diseases symptoms (70%), physical illnesses (67.1%), existing treatments (65%), and diagnosis (63.1%). Conclusion. The results showed that participants in this study usually searched for illnesses, symptoms, and treatments after they got sick and paid little attention to other aspects related to integral health.How to cite this article: KHademian F, Roozrokh M, Aslani A. Web-based health Information Seeking and eHealth Literacy among College students. A Self-report study. Invest. Educ. Enferm. 2020. 38(1):e08.

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.

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 The Swedish Version of the Electronic Health Literacy Scale: Prospective Psychometric Evaluation Study Including Thresholds Levels

10.2196/16316 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e16316
Author(s):  
Josefin Wångdahl ◽  
Maria Jaensson ◽  
Karuna Dahlberg ◽  
Ulrica Nilsson
Keyword(s):  
Convergent Validity ◽  
Health Resources ◽  
Psychometric Evaluation ◽  
Evaluation Study ◽  
Self Report ◽  
Swedish Version ◽  
Instrument Making ◽  
Ehealth Literacy ◽  
Threshold Levels ◽  
Electronic Health

Background To enhance the efficacy of information and communication, health care has increasingly turned to digitalization. Electronic health (eHealth) is an important factor that influences the use and receipt of benefits from Web-based health resources. Consequently, the concept of eHealth literacy has emerged, and in 2006 Norman and Skinner developed an 8-item self-report instrument to measure these skills: the eHealth Literacy Scale (eHEALS). However, the eHEALS has not been tested for reliability and validity in the general Swedish population and no threshold values have been established. Objective The aim of this study was to translate and adapt eHEALS into a Swedish version; evaluate convergent validity and psychometric properties; and determine threshold levels for inadequate, problematic, and sufficient eHealth literacy. Methods Prospective psychometric evaluation study included 323 participants equally distributed between sexes with a mean age of 49 years recruited from 12 different arenas. Results There were some difficulties translating the English concept health resources. This resulted in this concept being translated as health information (ie, Hälsoinformation in Swedish). The eHEALS total score was 29.3 (SD 6.2), Cronbach alpha .94, Spearman-Brown coefficient .96, and response rate 94.6%. All a priori hypotheses were confirmed, supporting convergent validity. The test-retest reliability indicated an almost perfect agreement, .86 (P<.001). An exploratory factor analysis found one component explaining 64% of the total variance. No floor or ceiling effect was noted. Thresholds levels were set at 8 to 20 = inadequate, 21 to 26 = problematic, and 27 to 40 = sufficient, and there were no significant differences in distribution of the three levels between the Swedish version of eHEALS and the HLS-EU-Q16. Conclusions The Swedish version of eHEALS was assessed as being unidimensional with high internal consistency of the instrument, making the reliability adequate. Adapted threshold levels for inadequate, problematic, and sufficient levels of eHealth literacy seem to be relevant. However, there are some linguistic issues relating to the concept of health resources.

Identifying Patterns in Complex Field Data

2017 ◽  
Vol 225 (3) ◽  
pp. 268-284 ◽  
Author(s):  
Andrew J. White ◽  
Dieter Kleinböhl ◽  
Thomas Lang ◽  
Alfons O. Hamm ◽  
Alexander L. Gerlach ◽  
...  
Keyword(s):  
Heart Rate ◽  
Panic Disorder ◽  
Latent Class ◽  
Treatment Protocol ◽  
Situational Factors ◽  
Self Report ◽  
Complex Field ◽  
Cluster Assignment ◽  
Experimental Conditions ◽  
Conventional Analysis

Abstract. Ambulatory assessment methods are well suited to examine how patients with panic disorder and agoraphobia (PD/A) undertake situational exposure. But under complex field conditions of a complex treatment protocol, the variability of data can be so high that conventional analytic approaches based on group averages inadequately describe individual variability. To understand how fear responses change throughout exposure, we aimed to demonstrate the incremental value of sorting HR responses (an index of fear) prior to applying averaging procedures. As part of their panic treatment, 85 patients with PD/A completed a total of 233 bus exposure exercises. Heart rate (HR), global positioning system (GPS) location, and self-report data were collected. Patients were randomized to one of two active treatment conditions (standard exposure or fear-augmented exposure) and completed multiple exposures in four consecutive exposure sessions. We used latent class cluster analysis (CA) to cluster heart rate (HR) responses collected at the start of bus exposure exercises (5 min long, centered on bus boarding). Intra-individual patterns of assignment across exposure repetitions were examined to explore the relative influence of individual and situational factors on HR responses. The association between response types and panic disorder symptoms was determined by examining how clusters were related to self-reported anxiety, concordance between HR and self-report measures, and bodily symptom tolerance. These analyses were contrasted with a conventional analysis based on averages across experimental conditions. HR responses were sorted according to form and level criteria and yielded nine clusters, seven of which were interpretable. Cluster assignment was not stable across sessions or treatment condition. Clusters characterized by a low absolute HR level that slowly decayed corresponded with low self-reported anxiety and greater self-rated tolerance of bodily symptoms. Inconsistent individual factors influenced HR responses less than situational factors. Applying clustering can help to extend the conventional analysis of highly variable data collected in the field. We discuss the merits of this approach and reasons for the non-stereotypical pattern of cluster assignment across exposures.

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.

Resilient Coping Types in People With Spinal Cord Injury: Latent Class Analysis

2021 ◽  
pp. 003435522199073
Author(s):  
Chungyi Chiu ◽  
Jessica Brooks ◽  
Alicia Jones ◽  
Kortney Wilcher ◽  
Sa Shen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Life Satisfaction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Psychosocial Interventions ◽  
Self Report ◽  
Class Analysis ◽  
Multivariate Tests ◽  
Cord Injury

Resilience is central to living well with a spinal cord injury (SCI). To provide a timely, targeted, and individualized intervention supporting resilience, it is necessary to assess an individual’s resilience level and characteristics of resilience on an ongoing basis. We aimed to validate the different types of resilient coping among people with SCI (PwSCI), using the Connor–Davidson resilience scale, and to identify the relationships between resilience and other psychosocial factors among the types of resilient coping. We recruited 93 PwSCI, who took the self-report measures of resilience, depression, life satisfaction, and spirituality. Using latent class analysis, we found three types: (a) goal-pursuing, bouncing-back, and persevering, named GP; (b) uncertainty about coping with setbacks, named UC; and (c) loss of resilient coping, named LOSS. The multivariate tests indicated that the three types differed on a linear combination of resilience, depression, and life satisfaction, with a large effect size. We discussed the three types of resilient coping and the implications for psychosocial interventions. We also recommended that rehabilitation clinicians examine PwSCI’s resilience levels and types of resilience during initial and follow-up visits. In doing so, PwSCI will have timely, targeted supports for developing and/or re-building their resilience.

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