Electronic Health Literacy as a Source of Self-Efficacy Among Community-Dwelling Older Adults

ABSTRACT

Objectives

Health-related factors, such as health status, health anxiety, and health literacy, are established contributors to self-efficacy. However, the relationship between electronic health literacy and self-efficacy is less known. The present study examined the role of electronic health literacy in relation to self-efficacy among community-dwelling older adults.

Methods

Cross-sectional survey data were collected in the United States between September 2022 and March 2023. The survey dataset consisted of 191 responses from individuals in the United States who were ages 65 or older. It provided information about survey respondents’ sociodemographic status, perceived health status, health anxiety, electronic health literacy, and self-efficacy. Hierarchical linear regression was conducted to analyze the data.

Results

Electronic health literacy was positively related to self-efficacy, and health anxiety was negatively related to self-efficacy, with sociodemographic status and perceived health status controlled.

Conclusions

The results indicate that electronic health literacy can be a source of self-efficacy among community-dwelling older adults.

Clinical Implications

Improving older adults’ electronic health literacy may help them maintain self-efficacy, and the improvement should be made, especially in the domains of evaluating health information found on the internet and making decisions based on the information.

KEYWORDS:

• Digital literacy
• health literacy
• health anxiety
• perceived health status
• health education

Self-efficacy is a critical factor for health outcomes. It is a set of beliefs that sustain one’s expectation about taking a particular action and producing a specific outcome (Bandura, 1977; Farley, 2020). While sociodemographic status, such as age, gender, and education attainment, was reported to be associated with one’s self-efficacy by multiple studies (Bonsaksen et al., 2019; Gecas, 1989; Ribeiro et al., 2019; Woodward & Wallston, 1987), Bandura (1977), the developer of this concept, asserted that self-efficacy was a product of personal experiences rather than of personality itself. In this regard, individuals with a health condition are encouraged to develop self-efficacy as it can improve their self-care abilities throughout the treatment process (Cutler et al., 2018). The impacts of self-efficacy on long-term health behaviors, such as compliance with exercise regimens, are emphasized as well (Sheeran et al., 2016).

Health-related contributors to self-efficacy

Healthcare practitioners aim to maximize treatment outcomes by implementing interventions that will increase patients’ self-efficacy. Research findings have revealed that individuals’ health status and self-efficacy is significantly associated. For instance, the relationship was examined between the health status of patients with a coronary disease and their self-efficacy in managing challenges posed by the disease (Sarkar et al., 2007). As a result, the patients’ self-efficacy scores were positively associated with their overall health status after the disease severity was adjusted (Sarkar et al., 2007). A positive association was also found between health status and self-efficacy among patients with arthritis (Cross et al., 2006).

Different forms of anxiety are the well-known contributor to poor self-efficacy as well. Patients with type 2 diabetes in Taiwan showed a negative correlation between general anxiety and self-efficacy (Wu et al., 2013). Trait anxiety and state anxiety were negatively correlated with self-efficacy among advanced cancer patients. For instance, self-efficacy among patients undergoing chemotherapy for breast or colorectal cancer in England was significantly associated with decreased state anxiety throughout chemotherapy (Papadopoulou et al., 2017). Patients undergoing hemodialysis in Japan also showed a negative correlation between their hospital anxiety and self-efficacy (Mystakidou et al., 2010; Takaki et al., 2003). These previous findings led the author to hypothesize that health anxiety may negatively impact on one’s self-efficacy, as other forms of anxiety do, although the relationship between health anxiety and self-efficacy had not been reported.

Health anxiety is one form of anxiety that has drawn increased attention since the COVID-19 pandemic. It is defined as a heightened health concern that one has, or may develop, a serious medical condition, based on the misinterpretations of bodily sensations (American Psychiatric Association, 1994). Varying along a continuum, mild degrees of health anxiety can be relatively adaptive, but more severe degrees of health anxiety, which are often characterized by preoccupation and worry, may impair social and occupational functioning (Taylor et al., 2004). Although older adults do not always develop higher health anxiety than younger people, they are more prone to health anxiety when they have one or more chronic conditions (Gerolimatos & Edelstein, 2012; Kim et al., 2001). Experiencing health problems could elevate the likelihood of overestimating the consequences of their health problems and developing generalized health worry (El-Gabalawy et al., 2013). The direct relationship between health anxiety and self-efficacy has not been reviewed in prior research, but its deleterious effects on the quality of life are well-documented (Gerolimatos & Edelstein, 2012).

Health literacy, on the other hand, may contribute to an increase in individuals’ self-efficacy. Health literacy is defined as the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others (Centers for Disease Control and Prevention, 2023a). It is especially important when a person is overwhelmed by new information accompanied by a diagnosis (Farley, 2020). As today’s healthcare system requires patients to make shared decisions with their physician, the patients need to adequately understand the benefits and risks of their treatment options (Torres & Marks, 2009). Health literacy empowers patients to understand those benefits and risks and to play a role in making the most optimal decision (Torres & Marks, 2009). Hence, researchers predicted that health literacy would be significantly associated with self-efficacy, but their studies resulted in mixed findings. Health literacy was not found to be significantly associated with self-efficacy among patients with health failure and patients with hypertension (Chen et al., 2014; Osborn et al., 2011). In contrast, health literacy was positively associated with self-efficacy among patients with health failure and older diabetic patients (Macabasco O’Connell et al., 2011; Roshan et al., 2023).

Growing importance of electronic health Literacy

Relatively to general health literacy, electronic health literacy is a newer concept that has not been widely studied (Karnoe & Kayser, 2015). Also known as ehealth literacy, electronic health literacy refers to the ability to find, understand, and evaluate information through electronic resources to identify and solve health problems (Norman & Skinner, 2006b). Some propose to use the term “digital health literacy,” “media health literacy,” or “health technology literacy” because ehealth literacy encompasses computer and media literacy as well as traditional health literacy (European Commission, 2014; Jordan-Marsh, 2010; Levin-Zamir & Bertschi, 2018). eHealth literacy has become almost essential for interacting with the healthcare system as a considerable amount of health information is circulated in electronic forms under today’s digital and media saturated environments (Levin-Zamir & Bertschi, 2018).

Individuals equipped with ehealth literacy are able to utilize the internet and digital technologies to navigate health information; in addition, they should be able to evaluate the obtained information and apply trustworthy information to their health problems because some online sources, such as social media platforms, can deliver inaccurate health information (Norman & Skinner, 2006b; Park et al., 2024). eHealth literacy is especially crucial for certain populations, such as older adults, given the increasing risks of comorbidity as people age (Ferris et al., 2018). However, older adults are reported to have lower ehealth literacy than younger people, due to their lack of skills to utilize digital technologies (Neter & Brainin, 2012; Richtering et al., 2017; Xesfingi & Vozikis, 2016).

Despite the acknowledgment of ehealth literacy and its growing importance, little evidence is available about whether ehealth literacy serves as a contributor to self-efficacy as general health literacy can do. Whereas a vast majority of previous studies addressed the relationship between general health literacy and self-efficacy, only one study provided some evidence about ehealth literacy and self-efficacy, reporting that ehealth literacy was positively associated with self-efficacy (Filabadi et al., 2020). The study was conducted to identify the correlation between ehealth literacy, quality of life, and self-efficacy, and the participants of the study were 400 outpatients of two community health centers in Iran, whose ages ranged between 17 and 75 (Filabadi et al., 2020).

Since most previous studies regarding health literacy and ehealth literacy focused on clinical populations, the present study aimed to generate knowledge about older adults’ ehealth literacy in a non-disease-specific context. eHealth literacy is considered a critical asset for older adults because even healthy older adults are faced with the higher risks of chronic conditions than their younger adults. For this aim, it tested the hypothesis that ehealth literacy would be positively associated with self-efficacy among community-dwelling older adults when other health-related contributors, including sociodemographic status, perceived health status, and health anxiety, were considered.

Methods

Data and sample

After the Institutional Review Board at The Ohio State University approved this study, survey data were collected between September 2022 and March 2023. Eligible survey participants included individuals ages 65 or older, who were able to read English and were not cognitively impaired. Before participation, those interested in this study were asked to indicate if they had been diagnosed with cognitive disorder. As part of a purposeful sampling strategy, flyers were distributed at churches, senior centers, and community centers located in Ohio to invite survey participants. Eligible participants were required to sign the written consent form and to fill out the survey on hard copy. Those who completed the survey could opt into the lottery offering a $30 gift card as incentive. An arbitrary identification number was assigned to each retrieved survey for privacy purposes. In addition, the survey data were anonymized, coded, and stored in a password-protected datasheet. A listwise deletion was conducted to handle missing data, leaving a total of 191 survey responses. When the proportion of the cases with missing data is small (e.g., less than 5%), it is suggested that the listwise deletion can be a strategy to handle missing data, with no or small biases in the effect estimates (Schafer, 1999).

Measures

Self-efficacy

Participants in this study were asked to report their self-efficacy, which was measured with the General Self-Efficacy Scale. Developed and validated by Schwarzer and Jerusalem (1995) to assess one’s general sense of self-efficacy in coping with daily hassles and adapting to stressful life events, the measure employs a four-point Likert scale (1 = not at all true; 2 = hardly true; 3 = moderately true; 4 = exactly true). The participants’ responses to 10 items (e.g., “I can always manage to solve difficult problems if I try hard enough.”) yielded a composite score ranging from 10 to 40, with higher scores demonstrating higher levels of self-efficacy. Cronbach’s Alpha ($\mathit{\alpha }$ = .90) indicated that the measurement of self-efficacy had good internal reliability with this sample.

Perceived health status

The author developed one item (i.e., “How would you rate your overall health status?”) to assess perceived health status among study participants. This item was answered on a five-point Likert scale (1 = very poor; 2 = poor; 3 = average; 4 = good; 5 = very good).

Health anxiety

The Whiteley Index was adopted to measure health anxiety among study participants. Developed in the 1960s, it is considered a classic scale of hypochondria (Chen et al., 2021). Based on the diagnosis criteria of hypochondriasis, the Whiteley Index consists of 14 items assessing one’s disease phobia, somatic preoccupation, and disease conviction (Pilowsky, 1967). The study participants were asked to answer yes (coded as “1”) or no (coded as “0”) to the total of 14 items as they were all closed questions, such as “Do you often worry about the possibility that you have got a serious illness?” The participants’ responses to 14 items yielded a composite score ranging from 0 to 14, with higher scores demonstrating higher levels of health anxiety. Cronbach’s Alpha ($\mathit{\alpha }$ = .81) indicated that the Whiteley Index with this sample had good internal reliability.

eHealth literacy

eHealth literacy was measured with the eHealth Literacy Scale (eHEALS), which was developed by Norman and Skinner (2006a) and was widely adopted for the measurement of ehealth literacy. Consisting of eight items, eHEALS measures one’s knowledge, comfort level, and perceived skills in finding, evaluating, and applying electronic health information to health problems. Respondents of eHEALS should report their confidence in using health information on the internet to make health decisions, as well as their abilities to locate and evaluate the information. The study participants were asked to answer all items, using a five-point Likert scale (1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; 5 = strongly agree). Their responses yielded a composite score ranging from 8 to 40, with higher scores demonstrating higher levels of eHealth literacy. Each items was coded from 1 to 5. The first three items concerned individuals’ ability to find useful health information on the internet. The fourth and fifth items were designed to assess individuals’ ability to make use of the information for their own benefit. The last three items concerned more advanced skills, such as evaluating the quality of health information on the internet and applying the information to making health decisions. According to Cronbach’s Alpha ($\mathit{\alpha }$ = .95), the measurement of ehealth literacy demonstrated good internal reliability with this sample.

Analytic procedure

STATA 16.0 was utilized for statistical analyses of the survey data. Descriptive information of the data were first reviewed, and then the correlations among major explanatory variables (i.e., perceived health status, health anxiety, and ehealth literacy) were examined. Lastly, hierarchical linear regression analysis was conducted. While self-efficacy was the outcome variable for the regression models of this study, other variables were explanatory variables, including sociodemographic status (i.e., age, gender, race or ethnicity, education attainment, and household income), perceived health status, health anxiety, and ehealth literacy. As noted earlier, all these explanatory variables but ehealth literacy were reported to be associated with self-efficacy by multiple previous studies. To fulfil the aim of identifying the relationship between ehealth literacy and self-efficacy, the other explanatory variables than ehealth literacy were treated as covariates in the regression models.

Hierarchical regression analysis is a sequential process involving the entry of explanatory variables into the analysis in a stepwise manner (Lewis, 2007). It is often used for evaluating the contributions of explanatory variables beyond previously entered variables, for statistical control and for examining incremental validity (Lewis, 2007). To take advantage of hierarchical regression analysis, the explanatory variables of this study were entered at different levels. The first model included sociodemographic status and perceived health. Health anxiety and ehealth literacy respectively were added into the second and the final model. While the final model is the focus of this study, the first and second models can enhance current knowledge of the relationships between sociodemographic status, other health-related factors, and self-efficacy.

Results

Sample characteristics

Sample characteristics are presented in Table 1. Study participants’ mean age was 70.87 (SD = 4.30). Female participants (62.8%) considerably outnumbered male participants (37.2%). The vast majority of the participants identified themselves as White (95.8%), with Hispanic/Latine (2.6%) and Black/African American (1.6%) participants accounting for the rest. About half of the participants (50.3%) were highly educated, reporting to have a bachelor’s, graduate, or professional degree. When asked about their monthly household income, a large proportion of the participants indicated that it was $7,000 or more (26.2%), followed by those who indicated that it ranged between $3,000 and $4,999 (24.6%).

Table 1. Sample characteristics.

Variable	Sample Size (n = 191)
	n	%
Age
65–69	66	34.6
70–79	68	35.6
80–89	48	25.1
90–94	9	4.7
Gender
Male	71	37.2
Female	120	62.8
Other	0	0
Race
White	183	95.8
Black/African American	3	1.6
Hispanic/Latine	5	2.6
American Indian/Alaskan or Hawaiian native	0	0
Asian or other Pacific islander	0	0
Other	0	0
Education Attainment
Less than high school	3	1.6
High school diploma/GED	22	11.5
Post-secondary technical training	9	4.7
Some college	50	26.2
Associate degree	11	5.8
Bachelor’s degree	54	28.3
Graduate/Professional degree	42	22
Prefer not to answer	0	0
Household Income
Less than $999	2	1
$1,000 to $1,999	15	7.9
$2,000 to $2,999	23	12
$3,000 to $4,999	47	24.6
$5,000 to $6,999	38	19.9
$7,000 or more	50	26.2
Prefer not to answer	16	8.4

Variable characteristics

Means and standard deviations of variables, and the correlations between the variables are presented in Tables 2 and 3. The sample exhibited mean scores that were higher than the medians for self-efficacy (M = 30.45, SD = 4.81) and health anxiety (M = 4.16, SD = 3.09). On the other hand, the means of ehealth literacy (M = 27.63, SD = 7.82) and perceived health status (M = 3.75, SD = .78) were lower than their medians. Medians of self-efficacy, health anxiety, ehealth literacy, and perceived health status were 30, 3, 29, and 4, respectively. Each item score of eHEALS, as well as the composite score, was reviewed. All eight items were coded from 1 to 5, and their mean was 3.46. The first three items concerning the ability to find health information on the internet were scored the highest, ranging between 3.61 and 3.65, whereas the last three items concerning the ability to evaluation health information were scored the lowest, ranging between 3.15 and 3.35.

Table 2. Mean and standard deviation of variables.

Variable	Range	M	SD
Self-Efficacy	10–40	30.45	4.81
Perceived Health Status	1–5	3.75	.78
Health Anxiety	0–14	4.16	3.09
eHealth Literacy	8–40	27.63	7.82

Table 3. Correlation coefficients between variables.

	Perceived Health Status	Health Anxiety	eHealth Literacy
Perceived Health Status	1
Health Anxiety	−.42***	1
eHealth Literacy	.19**	0.02	1

***p < .001, **p < .01.

Pearson correlation coefficients in Table 3 indicate the strength of the linear relationship between two variables. According to the coefficients, perceived health status was negatively related to health anxiety (r = −.42, p < .001) and was positively related to ehealth literacy (r = .19, p < .01). Health anxiety and ehealth literacy were not significantly correlated.

Hierarchical linear regression analysis

Table 4 describes the hierarchical linear regression analysis results. The first model included sociodemographic status variables and perceived health status, indicateing that age (β = −.23, p < .01) was negatively associated with self-efficacy. Education attainment (β = .21, p < .01) and perceived health status (β = .20, p < .01) were positively associated with self-efficacy. When health anxiety was added to the second model, the adjusted R-squared value increased from 10% to 13%. In this model, health anxiety was negatively associated with self-efficacy (β = −.22, p < .01). Age (β = −.28, p < .001) and education attainment (β = .18, p < .05) still showed a significant correlation with self-efficacy, but perceived health status was not significantly associated with self-efficacy any longer. When all explanatory variables were tested in the final model, they altogether explained 19% of the outcome variable’s variance. In this final model, sociodemographic status variables did not show a significant association with self-efficacy. Health anxiety (β = −.24, p < .01) was negatively associated with self-efficacy whereas ehealth literacy (β = .30, p < .001) was positively associated with self-efficacy.

Table 4. HierarchicaL linear regression analysis results.

Variable	First Model			Second Model			Final Model
	B	Β	SE	B	Β	SE	B	Β	SE
Constant	33.63		3.78	39.44		4.29	30.99		4.66
Age	−.15	−.23**	.05	−.17	−.28***	.05	−.09	−.15	.05
Female^a	.40	.04	.71	.19	.02	.70	−.01	−.00	.68
Black/African American^b	−3.65	−.09	2.68	−2.58	−.07	2.67	−1.87	−.05	2.57
Hispanic/Latine^b	−.60	−.02	2.11	.27	.01	2.10	−.03	−.00	2.03
Education Attainment	.60	.21**	.20	.51	.18*	.20	.29	.10	.20
Household Income	.00	.04	.00	.00	.02	.00	.00	.04	.00
Perceived Health Status	1.25	.20**	.44	.75	.12	.47	.39	.06	.46
Health Anxiety				−.35	−.22**	.13	−.37	−.24**	.12
eHealth Literacy							.18	.30***	.05
Adjusted R^2	.10			.13			.19

^aReference = Male,^b Reference = White. B = Unstandardized Beta, β = Standardized Coefficients Beta, ***p < .001, **p < .01, *p < .05.

Discussion

The hypothesis of this study was supported, as the data analysis results revealed that ehealth literacy was positively associated with self-efficacy among community-dwelling older adults when other covariates were considered. The negative correlation between perceived health status and health anxiety according to Pearson correlation coefficient can be interpreted as older adults feeling anxious about their health symptoms when their overall health is perceived to be poor. As demonstrated by the study of El-Gabalawy et al. (2013), previous illnesses may elevate the likelihood of one’s overestimating the consequences of health symptoms. On the other hand, the positive correlation between perceived health status and ehealth literacy can be attributed to the fact that older adults with higher ehealth literacy tend to better manage their health and prevent a further development of health problems (Centers for Disease Control and Prevention, 2023ba). As a result, they are likely to report better health status than those with lower ehealth literacy.

The first and second regression models revealed that younger ages and higher education attainment were significantly associated with higher self-efficacy. These results are consistent with previous research findings that older adults tend to have lower levels of self-efficacy than their younger counterparts and that individuals with higher education have higher levels self-efficacy than those with less education (Bonsaksen et al., 2019; Gecas, 1989; Woodward & Wallston, 1987). The final regression model of the present study shows that ehealth literacy was positively associated with self-efficacy and that health anxiety was negatively associated with self-efficacy. Other explanatory variables were not significantly associated with self-efficacy any longer. Since the relationship between health anxiety and self-efficacy has been little studied, this finding (i.e., negativeassociation between health anxiety and self-efficacy) contributes to the literature. It can be interpreted that excessive health concerns could impact older adults’ lifestyle in many aspects, negatively affecting their general sense of confidence.

The regression analysis results indicate that ehealth literacy can be a source of self-efficacy among community-dwelling older adults, regardless of their perceived health status, health anxiety, and sociodemographic status. Problematically, older adults are known to have lower levels of ehealth literacy than younger adults (Neter & Brainin, 2012; Richtering et al., 2017; Xesfingi & Vozikis, 2016). This is mainly relevant to the digital divide, which remains a social problem in the United States and many other countries (Van Deursen & Van Dijk, 2019). The digital divide is caused not only by the gap in access to digital technologies, but also by the gap in the ability to engage with digital technologies in meaningful ways (Hargittai et al., 2019). Along with education and employment status, age contributes to a large proportion of the digital divide (Attewell, 2001; Blank & Groselj, 2014). This is called the gray digital divide, highlighting that a larger number of older adults lack the knowledge and skills for utilizing digital technologies than young people who often are digital natives (Blank & Groselj, 2014; Lagacé et al., 2016; Neves et al., 2018). Pew Research Center reported in 2021 that a quarter of American adults ages 65 and older never used the internet, compared to 7% of those ages between 18 and 64 who did not use the internet (Perrin & Atske, 2021).

In response, intervention strategies have been developed to help older adults improve ehealth literacy, with an emphasis on their abilities to utilize digital technologies. Nahm et al. (2019), for instance, designed a randomized controlled trial intervention targeting 272 older adults. After participating in a three-week online learning program, the older adults showed an improvement in knowledge about ehealth literacy, as well as decision-making and communication for healthcare utilization (Nahm et al., 2019). A systematic review identified specific domains of ehealth literacy those interventions focused on. Most interventions focused on the ability to use technologies to process health information and the ability to understand health concepts and language (Cheng et al., 2020). Motivation to engage with digital technologies was frequently addressed as well (Cheng et al., 2020).

The author suggests that those interventions be implemented at the community level. In particular, communities with higher rates of older and other vulnerable residents (e.g., less-educated, low-income households) should implement the interventions on a regular basis, instead of keeping them a one-time event. Adult learning centers can be an appropriate space for offering the interventions. Should a community lack necessary resources (e.g., space, instructors, and the like), one-on-one mentoring can be a feasible strategy. Intergenerational one-on-one mentoring programs have successfully engaged technology-savvy youth volunteers to help older adults improve digital literacy (Henner, 2009; Kaše et al., 2019). According to a meta-analysis (Dong et al., 2023), there are seven studies that have reported the effectiveness of experimental interventions to increase older adults’ ehealth literacy. Those studies were conducted in different countries (e.g., The United States, Italy, Taiwan, and South Korea) and were published from 2011 to 2022. A variety of strategies were applied to the interventions, such as collaborative group learning sessions in public libraries, intergenerational mentoring, and a theory-based online learning program.

The author also suggests that the ehealth literacy domain of health information evaluation be emphasized by the interventions. Most ehealth literacy interventions sought to enhance the ability to locate and understand health information on the internet (Cheng et al., 2020). Although the ability to properly evaluate acquired information was overlooked, it is equally important, given the internet flooded with health information sources. Results of the present study indicate that older adults’ skills are poorer in the domain of evaluating health information on the internet, compared to their skills in the domain of simply locating health information. Individuals should be considered adequately equipped with ehealth literacy if they can tell reliable information sources from unreliable ones.

The present study has several limitations. First, the use of nonprobabilistic sampling makes it difficult to generalize its findings to the whole older population. The sample in the present study mostly consisted of older adults with high levels of educational attainment and those who identified as White. Using a more representative sample might have led to different findings. For future research in this topic area, it is suggested that the probabilistic sampling method be used to produce knowledge applicable to the whole population. Second, the finding of the association between ehealth literacy and self-efficacy does not confirm the causality between the two. While the present study adopted the correlational design as a starting point to explore this emerging topic, different research methods, such as longitudinal and experimental designs, can be used for confirming the causaliy. Specifically, research using the experimental design is known to be one of the most powerful methods for establishing causal relationships between variables. Experimental designs can establish a causal relationship by observing if the cause precedes the effect (Kirk, 2009).

In conclusion, the present study sheds light on ehealth literacy as a source of self-efficacy. It provides empirical evidence that ehealth literacy can play a vital role in improving self-efficacy, along with other health-related contributors. The author recommends integrating ehealth literacy education into treatment processes, as those processes often utilize strategies to enhance patients’ self-efficacy. It is hoped that the present study will help healthcare practitioners develop strategies for better health outcomes for their older clients.

Clinical implications

• Improving older adults’ ehealth literacy may enable them to maintain self-efficacy when they have health anxiety or perceive their health status to be poor.

• Older adults’ ehealth literacy needs improvements in the domains of evaluating health information on the internet and making decisions based on the information.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [CP], upon reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.