The Association of Graph Literacy With Use of and Skills Using an Online Personal Health Record in Outpatient Veterans

Ruiz, Jorge G.; Andrade, Allen D.; Hogue, Christie; Karanam, Chandana; Akkineni, S.; Cevallos, David; Anam, Ramanakumar

doi:10.1080/10810730.2016.1193915

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…Twenty publications (19 studies) examined the influence of 52 factors on subjectivelymeasured use of ePHRs [32,47,50,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. These factors were grouped into four main categories according to Or and Karsh's conceptual framework [25]: 35 personal factors, 9 human-technology interaction factors, 7 organisational factors, and 1 social factor.…”

Section: Factors Affecting Subjectively-measured Usementioning

confidence: 99%

Factors that affect the use of electronic personal health records among patients: A systematic review

Abd‐Alrazaq

Bewick

Farragher

et al. 2019

International Journal of Medical Informatics

101

110

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ReuseThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can't change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. AbstractBackground: Electronic personal health records (ePHRs) are web-based tools that enable patients to access parts of their medical records and other services. In spite of the potential benefits of using ePHRs, their adoption rates remain very low. The lack of use of ePHRs among patients leads to implementation failures of these systems. Many studies have been conducted to examine the factors that influence patients' use of ePHRs, and they need to be synthesised in a meaningful way.Objective: The current study aimed to systematically review the evidence regarding factors that influence patients' use of ePHRs.Methods: The search included: 42 bibliographic databases (e.g. Medline, Embase, CINHAL, and PsycINFO), hand searching, checking reference lists of the included studies and relevant reviews, contacting experts, and searching two general web engines. Study selection, data extraction, and study quality assessment were carried out by two reviewers independently. The quality of studies was appraised using the Mixed Methods Appraisal Tool. The extracted data were synthesised narratively according to the outcome: intention to use, subjective measures of use, and objective measures of use. The identified factors were categorised into groups based on Or and Karsh's conceptual framework.Results: Of 5225 citations retrieved, 97 studies were relevant to this review. These studies examined more than 150 different factors: 59 related to intention to use, 52 regarding subjectively-measured use, and 105 related to objectively-measured use. The current review was able to draw definitive conclusions regarding the effect of only 18 factors. Of these, only three factors have been investigated in connection with every outcome, which are: perceived usefulness, privacy and security concerns, and internet access. Conclusion: Of the numerous factors examined by the included studies, this review concluded the effect of 18 factors: 13 personal factors (e.g. gender, ethnicity, and income), four humantechnology factors (e.g. perceived usefulness and ease of use), and one organisational factor (facilitating conditions). These factors should be taken into account by stakeholders for the successful implementation of these systems. For example, patients should be assured that the system is secure and no one can access their records without their permission in order to decrease their concerns about the privacy and security. Fur...

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Section: Factors Affecting Subjectively-measured Usementioning

confidence: 99%

Factors that affect the use of electronic personal health records among patients: A systematic review

Abd‐Alrazaq

Bewick

Farragher

et al. 2019

International Journal of Medical Informatics

101

110

View full text Add to dashboard Cite

show abstract

“…Yet, individuals with low graph literacy-the ability to understand graphically-presented information [7]-benefit to a lesser extent from graphical displays than individuals with high graph literacy. For instance, graph literacy can moderate the effectiveness of graphical displays in improving health risk comprehension [1,8,9], promoting healthmanagement tasks [10] and healthy behaviors [11,12]. Graph literacy is associated with doctors' and patients' self-reported use of graphs to communicate health risks to others [13] and use of health portals containing graphs [12].…”

mentioning

confidence: 99%

“…An impediment to a more thorough investigation of graph literacy is that existing measures are often too difficult or too long for the general population [24] [7]. This may discourage their use in research and medical practice [12], and make them difficult to implement in costly studies on representative samples. Indeed, previous studies have largely involved convenience samples of students [1,11,19,25] and people with high education [8,14,15,17], limiting the generalizability of findings.…”

mentioning

confidence: 99%

Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

et al. 2019

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Background: Visual displays can facilitate risk communication and promote better health choices. Their effectiveness in improving risk comprehension is influenced by graph literacy. However, the construct of graph literacy is still insufficiently understood, partially because existing objective measures of graph literacy are either too difficult or too long. Objectives: We constructed a new 4-item Short Graph Literacy (SGL) scale and examined how SGL scores relate to key cognitive, affective, and conative precursors of health behavior change described in common health behavior theories. Methods: We performed secondary analyses to adapt the SGL scale from an existing 13-item scale. The initial construction was based on data collected in a laboratory setting in Germany (n=51). The scale was then validated using data from nationally representative samples in Germany (n=495) and the United States (n=492). To examine how SGL scores relate to precursors of health behavior change, we performed secondary analyses of a third study involving a nationwide US sample comprised of 47% racial/ethnic minorities and 46% with limited formal education (n=835). Results: Graph literacy was significantly associated with cognitive precursors in theoretically expected ways (e.g., positive associations with risk comprehension and response efficacy, and a negative association with cognitive risk perception). Results for affective precursors generally mirrored those for cognitive precursors, although numeracy was a stronger predictor than graph literacy for some affective factors (e.g., feelings of risk). Additionally, graph literacy (but not numeracy) predicted key conative precursors such as defensive processing. Conclusions: Our data suggest that the SGL scale is a fast and psychometrically valid method for measuring objective graph literacy. Our findings also highlight the theoretical and practical relevance of graph literacy.

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“…Meanwhile, the frequency of using the mHealth apps was also self-reported by the respondents, following the previous research as well [9]. In addition, the validity of the self-reported method on the usage of mHealth apps has already been covered by Robbins et al [61] and Ruiz et al [62].…”

Section: Discussionmentioning

confidence: 95%

mHealth Apps Assessment among Postpartum Women with Obesity and Depression

2020

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Background: Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications. Objective: The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women. Methods: Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data. Results: Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p > 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage. Conclusion: The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used.

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The Association of Graph Literacy With Use of and Skills Using an Online Personal Health Record in Outpatient Veterans

Cited by 13 publications

References 30 publications

Factors that affect the use of electronic personal health records among patients: A systematic review

Factors that affect the use of electronic personal health records among patients: A systematic review

Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

mHealth Apps Assessment among Postpartum Women with Obesity and Depression

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