Increasingly, teenagers and young adults (TYAs) seek out health information online; however, it is not clear whether they possess electronic health  literacy, defined as “the ability to select, appraise, and utilize good quality health information from the internet.” A number of factors are included in the Lily model proposed by Norman and Skinner underpinning the development of eHealth literacy. It is important to understand which elements may influence the development of eHealth literacy in young people, as the current generation will continue to “Google it” when faced with a health problem throughout their lives.The objectives of this study are to explore potential factors influencing young people’s eHealth literacy and explore the underlying constructs of the eHealth Literacy Scale (eHEALS) in a population of UK university students.A total of 188 undergraduate psychology students from a large UK University were recruited as an opportunity sample. Of these, 88.8% (167/188) of participants were female with a mean age of 20.13 (SD 2.16) years and the majority were White British . Employing a cross-sectional design TYAs completed the following measures exploring eHealth literacy (eHEALS): Irrational Health Belief Scale; Newest Vital Sign (NVS), a measure of functional health literacy; Need for Cognition Scale, a preference for effortful cognitive activity; and General Self-Efficacy (GSE) Scale, exploring personal agency and confidence. The eHEALS was also subject to exploratory factor analysis (EFA), for which in addition to the total variance explained, the scree plot, eigenvalues, and factor loadings were assessed to verify the structure.r=0.28, P<.001) and hierarchical linear modeling revealed GSE as the significant predictor of scores on the eHEALS , accounting for 8.0% of the variance. Other notable relationships were GSE and need for cognition (NFC) were also positively correlated , and NFC and irrational health beliefs were significantly negatively correlated . Using Spearman correlations, GSE and NVS  and NFC and NVS  were positively correlated. An EFA revealed the scale to be stable and identified a 2-factor structure related to information acquisition and information application.eHEALS and GSE were significantly positively correlated (This is the first study in the UK to explore relationships between these key variables and verify the structure of the eHEALS in a TYA population in the UK. The findings that self-efficacy has a major influence firmly consolidate its status as fundamental to the development of eHealth literacy. Future studies will explore the influence of body image and the development of eHealth literacy in more diverse TYA populations. Almost all young adults in the UK are shown to be recent internet users  with teeIntuitively one would assume that young people as “digital natives” would be competent users of the technology they have been brought up with; however, this is not always the case . EquallyOne of the fundamental variables underpinning the Lily model is functional health literacy . FHL is an essential skill in navigating health information and pivotal to successful engagement with health resources over the internet. Recent studies have shown that there is a relationship between FHL and eHealth literacy; higher scores in FHL were correlated with higher scores on the eHEALS in a sample of Italian adults . Until rNorman and Skinner cite Feuerstein  who descHowever, it is not only cognitive capacity which underpins success in processing and utilizing health information. Generally, it is assumed that health and scientific information are processed rationally. However, some (particularly people with anxiety) have “irrational health beliefs” (IHBs) which can be explained by the cognitive–behavioral model of hypochondriasis ,31. PeopUnderpinning the previously mentioned components of the Lily model, Norman and Skinner state that electronic health literacy is born out of social cognitive theory proposed by Bandura , with seThe putative factors we have reviewed here all relate to young people’s experiences of help seeking via the internet and consequent health behavior. Further, these factors reflect the overlapping literacies of the Lily model proposed by Norman and Skinner , namely,With regard to the scale to measure eHealth literacy (eHEALS) , this beThe primary aim was to explore whether factors potentially underpinning the 6 facets of the Lily model   in a sample of university students in the UK.Participants (n=188) were undergraduate psychology students from a UK University recruited via opportunity sampling in research methods sessions. Of these, 167 participants were female (88.8%), with a mean age of 20.13 (SD 2.16) years. The majority of participants identified their ethnicity as White British 159/188 (84.6%) and said that English was the main language spoken at home . Other participants described their ethnicity as Mixed 4/188 (2.1%); Pakistani, Indian, or Asian 13/188 (6.9%); any other White background 2/188 (1.1%); or Black 2/188 (1.1%). The majority were unmarried 182/188 (96.8%) and living in shared accommodation 122/188 (64.9%). Most participants 139/188 (73.9%) had not seen a health professional in the past 2 weeks. Of those that had, most had seen a general practitioner  or a physiotherapist (n=4).how to use the Internet to answer my questions about health.” The responses range from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating a higher level of eHealth literacy.The eHEALS  is an 8-The NVS ,23 is a The Need for Cognition Scale  is an 18The Irrational Health Belief Scale  is a 20-“I can always manage to solve difficult problems if I try hard enough.” The measure has Likert scale scoring, with responses ranging from 1  to 4 (exactly true). The answers are summed, with total scores ranging from 10 to 40. A higher score indicates greater SE.The General Self-Efficacy (GSE)  scale isEthical approval from the Leeds Beckett Psychology Ethics Committee was granted on December 12, 2017 (number: PH/AW/121217). Participants took part in this study in order to generate data for them to analyze in a research methods module, and gave informed consent for these data to be used in further research. All measures were programmed into Qualtrics, a web-based survey tool, and participants completed these in-exam conditions during research methods sessions over the course of 1 week in March 2018.All data analyses were conducted using IBM SPSS Statistics (version 26). Internal consistency of the scales was examined using the Cronbach alpha coefficient. The psychometric properties of the eHEALS in a UK sample were explored using EFA. We initially examined correlations between all key variables. We had theoretical grounds to suggest that GSE would be a significant predictor of eHealth literacy, therefore a hierarchical multiple regression was performed by entering GSE first and then examining the impact of NFC, IHBs, NVS, and GSE on the outcome variable eHEALS. For the NVS, 18% of the data were missing, therefore mean scores were inputted; missing data were <0.01% for other variables.Data assumption tests were performed prior to conducting the linear regression, including Cook distance, collinearity, variance inflation factor, Durbin–Watson, and homoscedasticity. To test for normality, skewness and kurtosis values were computed prior to correlation analysis.An EFA was used to examine the eHEALS structural validity on the 8 data item set (N=188 cases). The minimum recommended sample size to conduct an EFA is 100  and if fDescriptive statistics for all key variables are presented in r=0.28, P<.001). No other significant relationships were noted between eHEALS and other key variables. The GSE and NFC scores were also positively correlated , and NFC and IHBs were significantly negatively correlated . Using Spearman correlations, GSE and NVS  and NFC and NVS  were positively correlated.Correlations between key variables are presented in F1,186=16.16, P<.001, R2=0.08). Model 2  explained a nonsignificant 0.6% increase in the variance . Together, both models explained 8.6% of the total variance. All assumptions of the regression analysis were met, and a hierarchical procedure was performed to assess if the variables GSE, NVS, IHB, and NFC could predict eHealth literacy measured by the eHEALS. GSE was entered first into the model (Model 1) and this explained a significant proportion (8.0%) of the variance in eHealth literacy , revealing collinearity levels, was not detrimental to the analysis, and thus, no items were removed.Using principal axis factoring an EFA was conducted on the 8 items with varimax rotation. The Kaiser–Meyer–Olkin measure verified sampling adequacy at 0.80 above the minimum criterion of 0.50, and all Kaiser–Meyer–Olkin values for individual items were 0.65 or more. The Bartlett test of sphericity was also significant at The eHEALS performed well in terms of psychometrics in this sample : we calcThe scree plot confirmed that two factors had Eigen values over the Kaiser criterion of 1 and together explained 64.6% of the variance. acquisition and information application.In this exploratory study we have been the first to investigate relationships and potential predictors of key variables influencing eHealth literacy sample of UK University students. A highly significant positive relationship was found between eHEALS and GSE scores, but eHealth literacy did not significantly correlate with any other factor. However, significant positive correlations between NFC and the NVS, and between NFC and GSE were demonstrated as well as a significant negative association between NFC and IHB. Our secondary aim was to explore the underlying construct of the eHEALS in a UK TYA sample using EFA, where we found 2 underlying factors within the scale related to information Despite the expected negatively skewed distribution indicating higher scores on the NVS in our student sample, FHL measured by the NVS was not found to be a significant predictor of eHealth literacy. Previously Del Giudice and colleagues  demonstrall young people [In line with the literature, those who scored highly in our sample on the NVS  also scored highly on the NFC scale. This suggests an overlap between these 2 variables in that someone who prefers effortful cognitive activity would be more likely to score highly on a functional literacy test . Our fing people  to enablThe significant negative association between NFC and IHBs was as predicted, as IHBs are usually strongly held and people displaying IHBs have a limited need or desire to acquire new information to challenge or inform these beliefs . AlthougSE has been shown to be a pivotal component in the adoption and execution of healthy behaviors , and thaWe found a lack of significant correlation and predictive impact of NFC on the outcome variable (eHEALS) in contrast to the US study of young university students in which an association between the 2 variables was demonstrated . It is uacquisition (items 1-3) and information application (items 4-8), concur with those of Soellner et al [Previously the eHEALS was thought to be a unidimensional scale ,17; howeer et al , who fouThis was a cross-sectional design and as such only measured eHealth literacy at one time point, although it may be more revealing to measure this longitudinally to explore temporal changes and responses to changing circumstance . The skewed distribution of the NVS data and low internal consistency ensure that we proceed with caution and thus cannot conclusively state that FHL does not influence eHealth literacy in a UK TYA population. However, we also must question “Does perceived eHealth literacy (as measured by the eHEALS) translate into competent performance on the internet to gain health information?” Others have also found that the expected strong positive relationships between eHEALS scores and internet performance were absent . This suIn this study, because GSE only accounted for a small proportion of variance in our predictive model, we must speculate that alternative, more potent predictors of eHealth literacy exist. Gilstad  has propWe are reassured that NHS England  is workiIn conclusion, this is the first study in the UK to explore relationships between these key variables in a TYA population, and to perform EFA on the eHEALS with a TYA sample in the UK. As such, we can confirm the stability of the scale. The findings that SE has a major influence on eHealth literacy should consolidate its status as underpinning the Lily model, and as a fundamental starting point from where eHealth literacy is developed and understood. Future studies will explore physical and mental health status, health SE, and body image as potential predictors of eHealth literacy in more diverse TYA populations.


In this issue of the Journal, a group of distinguished Nordic researchers, led by Anne Helene Garde and including four of our Associated Editors, present a discussion paper that originated from a workshop and provides detailed recommendations on night shift work . The recThe discussion paper is remarkable in that it provides clear and strong recommendations based on what the authors themselves call a “limited literature”, thus a limited scientific evidence on the risk of shift work for cancer and other health and safety risks. In a recent editorial, British researchers concluded that, due to heterogeneity of shift working in longitudinal studies, it is too difficult to draw a firm conclusion about the risk of breast cancer, let alone about an exposure threshold for night shift work . Yet, boFor the most important health and safety conditions the Nordic authors are concerned with – cancer, cardiovascular disease, diabetes, injuries and pregnancy-related outcomes – the evidence relies on observational studies. Although longitudinal studies on shift work increasingly use register-based exposure information on working hours patterns, often based on payroll data that is linked with registers in healthcare , 4, thesOn the other hand, exercising caution does not mean that one should abstain from making recommendations when evidence is based on observational studies only, in the hope that this would keep one on the safe side of scientific scrutiny. There is no safe side. In accordance with Paul Watzlawick’s famous quote that “one cannot not communicate” , it can International Journal of Epidemiology published a series of articles, discussion papers and letters on causal inference in epidemiology, in particular on the merits and limitations of the counterfactual “potential outcome approach”, which relies heavily on experiments whether induced by the researcher or natural changes in particular situations that may be interpreted as happening at random  system, which rates the certainty of evidence and the strengths of recommendations in systematic reviews, for example in articles -22. As Garticles , the quaarticles  - a metharticles , 26 - rearticles .The paper by Garde et al  is not a


One month after adoption, the cat developed two pea-sized (approximately 10 × 10 mm) subcutaneous non-alopecic nodules along the right lumbar spine and left clavicle. After a physical examination, the veterinarian collected blood for a complete blood count and serum biochemistry panel. Bloodwork revealed moderate eosinophilia  and mild elevation of blood urea nitrogen . All other parameters were within the normal reference ranges. Serology did not detect Dirofilaria immitis antigen, feline immunodeficiency virus antibody, or feline leukemia virus p27 antigen . Other health concerns were not noted at that time. A fine needle aspirate (FNA) was performed by the veterinarian showing a heterogeneous lymphoid population. Skin biopsies were also taken from the two nodules and it showed granulomatous and eosinophilic dermatitis with presumptive intralesional nematodes. Over the following month, additional sub-cutaneous nodules appeared, and the cat was referred to the Dermatology Service – Small Animal Hospital at the University of Florida – College of Veterinary Medicine for evaluation.A 2-year-old, neutered male domestic shorthair cat was adopted as a stray in Florida, USA. At adoption, the cat was treated for fleas  and no D. immitis antigen was detected. A modified Knott’s microfilariae examination was performed on 2 mL of whole blood, and microfilariae were not recovered. Serology was positive for D. immitis antibody  and rare Ancylostoma spp. ova were seen following centrifugal fecal flotation . An echocardiogram and three-view thoracic radiographs were also performed and did not show abnormalities in the pulmonary artery. An ophthalmic examination for larvae or adults nematodes of D. immitis was also performed and was unremarkable.The parasitological examination of biopsy samples by maceration of tissue and saline sedimentation revealed fragments of adult male and female nematodes with the anterior end consistent with ria spp. . A singl striata  . Polymerase chain reaction (PCR) amplification of the nematode 18S rRNA gene was done as previously described  and 96.84% homologous with D. immitis (AB973231), the COI gene product was 92.65% homologous with D. repens (GenBank # KR998259) and 90.73% homologous with D. immitis (AJ537512), and the 12S gene product was 88.38% homologous with D. repens (AJ544832) and 90.91% homologous with D. immitis (MH051846).The sequences were compared to those in GenBank , EMBL , and Data Bank of Japan  databases using BLASTN , was used as an outgroup. Bayesian analyses of nucleotide alignments were performed using MrBayes 3.2.6 on the CIPRES server, with gamma distributed rate variation and a proportion of invariant sites, and a general time reversible substitution model   was applied between the shoulders once monthly to treat the dirofilariasis.Oral (PO) doxycycline (10 mg/kg BW q24 hours) was initiated based upon published guidelines for the treatment of feline heartworms  of Dirofilaria sp. were isolated in the present case. However, circulating microfilariae were not detected.This case describes for the first time the clinical signs, diagnostics, and treatments for a domestic cat presenting with subcutaneous nodules and isolation of a Dirofilaria spp. are filarial nematodes belonging to the Onchocercidae family  and an arthropod intermediate host . For this reason, it is expected that cats infected with Dirofilaria sp. may continue to intermittently show nodules while the various larval stages are killed, and the adults spontaneously die.The life cycle of Sarcocystis sp. sarcocyst was also identified and surgically removed from the subcutaneous connective tissue of the dorsal neck, and Ancylostoma spp. ova were found in the fecal flotation preparation. Both of these infections were likely secondary to the cat’s diet as a stray , and the cat was subclinical for these infections. In this case, serology was positive for D. immitis antibody but neither D. immitis antigen nor circulating microfilariae were detected. This is not surprising considering the cat’s lifestyle in a heartworm endemic geographic region  hongkongensis (Pradeep et al. D. striata. However, parasite behavior and morphologic features in the current case match most consistently with this nematode, but without additional methodology, including intact adult worms, this cannot be confirmed.Few deposited sequences of the D. immitis maturation in cats and dogs, furthermore, its use in treatment is currently being utilized in field studies for D. repens in Northern Italy (Cringoli et al. D. striata. However, two new nodules were seen during treatment and monthly application of topical 10% imidacloprid/1% moxidectin was continued leading to resolution of the new nodules, with no new lesions identified after 1.5 years of treatment.Treatment recommendations for the current case were extrapolated from previous feline cutaneous dirofilariasis (Cornegliani et al. D. striata as a differential diagnosis for cutaneous nodules in cats. The sequence data obtained enables recognition of this nematode as distinct from other Dirofilaria spp., such as D. immitis and D. repens.In conclusion, surgical excision and monthly topical topical 10% imidacloprid/1% moxidectin correlated with resolution of nodules in this cat; future controlled studies are needed to determine treatment efficacy. Clinicians in Northern America, especially those in the Southeastern United States, should consider


Evidence-based practice refers to building clinical decisions on credible research evidence, professional experience, and patient preferences. However, there is a growing concern that evidence in the context of electronic health  is not sufficiently used when forming policies and practice of health care. In this context, using evaluation and research evidence in clinical or policy decisions dominates the discourse. However, the use of additional types of evidence, such as professional experience, is underexplored. Moreover, there might be other ways of using evidence than in clinical or policy decisions.This study aimed to analyze how different types of evidence  obtained within the development and evaluation of an eHealth trial are used by diverse stakeholders. An additional aim was to identify barriers to the use of evidence and ways to support its use.This study was built on a case of an eHealth trial funded by the European Union. The project included 4 care centers, 2 research and development companies that provided the web-based physical exercise program and an activity monitoring device, and 2 science institutions. The qualitative data collection included 9 semistructured interviews conducted 8 months after the evaluation was concluded. The data analysis concerned (1) activities and decisions that were made based on evidence after the project ended, (2) evidence used for those activities and decisions, (3) in what way the evidence was used, and (4) barriers to the use of evidence.Evidence generated from eHealth trials can be used by various stakeholders for decisions regarding clinical integration of eHealth solutions, policy making, scientific publishing, research funding applications, eHealth technology, and teaching. Evaluation evidence has less value than professional experiences to local decision making regarding eHealth integration into clinical practice. Professional experiences constitute the evidence that is valuable to the highest variety of activities and decisions in relation to eHealth trials. When using existing scientific evidence related to eHealth trials, it is important to consider contextual relevance, such as location or disease. To support the use of evidence, it is suggested to create possibilities for health care professionals to gain experience, assess a few rather than a large number of variables, and design for shorter iterative cycles of evaluation.Initiatives to support and standardize evidence-based practice in the context of eHealth should consider the complexities in how the evidence is used in order to achieve better uptake of evidence in practice. However, one should be aware that the assumption of fact-based decision making in organizations is misleading. In order to create better chances that the evidence produced would be used, this should be addressed through the design of eHealth trials. Evidence-based medicine has taken a central role in health care, aiming to increase the quality of clinical practice. In the medical domain, it is conceptualized as building clinical decisions on credible research evidence, professional experience and judgement, and patient preferences -3. This Evidence produced by evaluations dominates the discourse related to the evidence-based practice of eHealth implementations ,12,13. TWhen the expectation is to use the evaluation evidence in making decisions regarding clinical implementations of eHealth , it refeFurthermore, the considered users of evidence in the context of eHealth are usually limited to policy makers or health care professionals. However, eHealth is a multi-disciplinary field , and theThe purpose of this study was to analyze how different types of evidence  are used by diverse stakeholders. An additional aim was to identify barriers to the use of evidence and ways to support its use.This study was built on a case of a multinational and interdisciplinary European Union–funded project . It was a nonpharmacological eHealth trial aimed at improving quality of life and increasing the independence of elderly with mild cognitive impairment and mild dementia. The Alpha trial introduced a case manager role and an ICT platform that consisted of web-based physical and cognitive exercise programs and an activity monitoring device to be used at home. As such, innovations were introduced on both the service model and technological levels.The Alpha approach was implemented and tested in 4 countries. The project involved 8 partners: 4 care centers (in the aforementioned countries), 2 research and development companies that provided the eHealth solutions, and 2 science institutions. The trial lasted for 6 months. The evaluation included a number of variables, such as clinical efficacy, quality of life, patient adherence to technology, patient and health care professional’s satisfaction, process effectiveness, and a cost-benefit analysis. Clinical professionals were asked to collect patient data using a number of standardized and custom-made questionnaires, as well as to register some data in the registries in Excel files. The evaluation was performed at the end of the project and was finalized in June 2018. During the evaluation, several project partners were charged analyzing the different variables. As it frequently happens in multinational projects, the evaluation had to overcome several practical circumstances such as ethical approvals and systems integration issues that delayed patient recruitment and the start of the trial. This created a situation in which the trial time had to be shortened for some patients, resulting in a smaller dataset than planned.The evidence considered in this study included evaluation results (including patient preferences) and professional experiences from the Alpha trial as well as existing scientific evidence from other research. Data were collected through 9 semistructured interviews with all the partners involved in the Alpha project  that were conducted 8 months after the evaluation was concluded. The stakeholders were delimited to the partners of the Alpha project, since they had deep knowledge and experience from the trial and they were the primary candidates to consider using the evidence developed. However, the funding institution did not require the project partners to use evidence from the Alpha project.The interviewee selection followed the principles of purposive sampling  and invoThe interviews followed a guide structured around the components of evidence use ,19,25, sFor every stakeholder, we analyzed the following: 1) the types of activities and decisions that were made after the Alpha project ended; (2) the types of evidence  that were used for those activities and decisions, based on the definitions of evidence  the type,17-19,22The findings were grouped by the types of activities and decisions made by the stakeholders using evidence from Alpha. For each activity or decision, the use of evidence is discussed.At 8 months after the Alpha project ended, partners in the project (stakeholders) used evidence in the following decisions and activities: (1) integrating or abandoning the Alpha approach in clinical practice, (2) publishing results from the Alpha study, (3) applying for new research funding, (4) supporting regional policymaking, (5) improving technology, and (6) teaching students and health care professionals. Next, the ways that evidence were used by the stakeholders in every decision and activity are explained.At the time of this study, 2 of the 4 care centers had decided to integrate the Alpha approach into clinical practice (care centers 1 and 2). In these care centers, the decision was mainly informed by the evidence from professional experiences. The health care professionals decided to adopt the Alpha approach based on their experiences with usability and adherence to the technology, as well as on patient satisfaction with the service. At the time of the decision, the evaluation results were not available yet. However, the professionals relied on their experience and the existing scientific evidence from other research that demonstrated that technologies and care models similar to Alpha can be beneficial for the patients targeted. The existing research was quite explicit on the benefits of physical and cognitive exercise (with and without the help of technology) for patients with cognitive impairments.Specific clinical data are not a reason to not try to implement this model. Existing research can provide us such information. <…> Data related to adherence are enough to be interpreted as a useful model for these patients.Care center 1, clinical neuropsychologistThe decisions to adopt the Alpha approach in care centers 1 and 2 were facilitated by the fact that resources for implementation were available from regional policies supporting and financing care models like Alpha. Care centers 1 and 2 planned to perform deeper statistical analyses on the clinical outcomes, cost, and savings. If a positive effect was found, the results would be disseminated, which would support their decision to integrate the Alpha approach in practice. Hence, in the case of care centers 1 and 2, professional experiences and existing scientific evidence from other research were used instrumentally (directly in decision making and action), while evaluation evidence was used symbolically .When the decision was made, we didn’t have any results yet. <...> Our experience and preliminary data showed that this model is quite good. <…> Managers trusted our previous evaluations of similar models and thought that it will be the same. <…> We have to redo the economic evaluation to see how much it actually costs and how much we save.Care center 2, quality directorCare center 3 planned to use the evaluation results to make a decision regarding adopting the Alpha approach in its clinical practice. The organization planned to present the evaluation results to the board and express a need for an eHealth solution like that tested in the Alpha project. Once approval from the board is obtained, the technology can be purchased and integrated in clinical practice. In this case, using the evaluation results in decision making for practice improvement indicates instrumental use.Once we demonstrate that the results are OK, we are in a position to escalate it to decision makers, and we are able to incorporate it in our organization.Care center 3, Head of eHealth researchCare center 4 decided to abandon the Alpha approach. Professional experiences were the primary influence on this decision. The concept was abandoned when the staff realized, over the course of the trial, how many resources the new model requires when applying it within the context of care center 4. It was deemed not the right time for the concept to be adopted in the organization. After the project finished, staff’s experiences in the care process of Alpha were presented to management. In the case of care center 4, professional experiences were used instrumentally (directly in decision making and action).We didn’t pay so much attention to the results of the evaluation. We looked at what does it mean to work with patients in a situation like that. <…> For management, the descriptive conclusions were more interesting than the analysis.Care center 4, project managerPublishing the results from the Alpha project in scientific outlets was initiated by almost all the partners of Alpha (except care center 4). The care centers used the clinical outcomes, quality of life, patient and employee satisfaction, and cost data in the scientific publications. The research and development companies used the adherence data and the feedback from patients and health care professionals related to their specific technology. Publishing the evaluation results helped these companies strengthen their image by demonstrating a case of the technology application in a real setting. In addition to the evaluation results, the partners used the existing scientific evidence from other research to build a case for research.Since scientific publishing did not directly relate to decision making in practice, such use of evaluation evidence and existing evidence from other research was deemed conceptual.Most of the partners  used Alpha evaluation results and professional experiences when applying for further research funding. Evaluation outcomes, experiences, and lessons learned in Alpha provided the basis for the case and allowed ideas to be built that could be applied in the next project. In such a case, Alpha evaluation results and professional experiences did not change local practices but increased the knowledge and understanding of the partners that subsequently helped to develop better research ideas and improve the design of the future studies. Hence, such use of evaluation evidence and professional experiences was deemed conceptual.Science institutions 1 and 2 and care centers 1, 2, and 3 presented Alpha results in regional policymaking activities as a concrete local example of an eHealth-supported care model within their regions. For this purpose, science institution 2 used the managerial and economic evidence from the evaluation of Alpha to demonstrate the possible impact of the eHealth solution on the local care facility. In addition, science institutions 1 and 2 used the health care professionals’ feedback and perceptions on working with Alpha in their local contexts to demonstrate local applicability. Since the Alpha case served as an example and was not meant to make decisions based on its results, such use of evaluation evidence and professional experiences in policymaking was deemed conceptual.When you discuss a real case here in <region>, these messages are stronger than to discuss cases in <another country> or to say that literature says these things are useful.Science institution 2, scientific coordinatorThe evaluation results of Alpha helped research and development company 2 in making decisions to improve its technology. The company focused on the patients’ and health care professionals’ feedback and preferences in relation to its technology and initiated actions to improve it. Since the evaluation evidence was used directly for decisions and action, we classified such use of evidence as instrumental.Professional experiences with Alpha were used by science institution 1 in teaching students and health care professionals. Science institution 1 relied on the professionals’ feedback and perceptions on working with Alpha in their local contexts, as it demonstrates local applicability. Since the Alpha case served as an example and decisions were not meant to be made based on its results, such use of professional experiences in teaching was deemed conceptual.We used the experience with the care models as an example in our courses. <…> We use it for practitioners as a subject to discuss and reflect upon.Science institution 1, director of the research centerThe first barrier to the use of evidence was related to the number of variables included in the Alpha evaluation. In most of the project locations, the scope of evaluation was deemed too extensive . The interviewees indicated that the time needed to collect this amount of data for every patient was too long. The clinicians had to fit the data collection into their routine work during meetings with the patients. Consequently, the clinicians were making choices about which data to collect at a particular time. Such trade-offs between data collection for the project and time spent with a patient affected the completeness of data collected and consequently the quality of evidence produced in the evaluation.When you want to monitor a lot of variables, it is directly related to the time you need to spend with the patients. <…> The target should be to optimize how we collect the variables and information using not that much time.Care center 3, Head of eHealth researchThe second barrier to the use of evidence was related to the Alpha evaluation design when comparing before-after situations. Some of the interviewees with a clinical background disagreed that eHealth integration decisions can be purely based on hard facts and not on evidence from the evaluation when making such a decision. According to these respondents, novel eHealth-supported care models tested during trials are complex dynamic systems within the local context that vary, have differences in culture, and are affected by social interaction. Since these care models cannot be assumed as stable controlled systems, the assumption of stability in the traditional before-after measurement design of an eHealth trial provides less valuable information for eHealth integration decisions to improve practice. Additionally, such an evaluation design comparing before-after situations does not maximize the potential to enhance local learning. The interviewees suggested that people’s experiences with an eHealth solution and process measures, both captured continuously, could lead to iterative adaptation and adjustment between the eHealth solution and the context. Such iterative evaluation would provide higher value in these eHealth integration decisions to improve practice.If we own the evaluation, we would take repeated measurements for improvement efforts and enhanced learning, rather than traditional approaches.Care center 2, quality directorIn this study, we analyzed how different types of evidence, generated through the development and evaluation of an eHealth trial, are used by diverse stakeholders. This work demonstrated that evidence from eHealth trials is used in more ways than decision making regarding clinical integration or policymaking. In addition, different stakeholders can use the evidence for scientific publishing and dissemination, eHealth technology improvement, research funding applications, and teaching.We found that professional experiences seem to have more influence over decisions regarding eHealth integration into clinical practice than formal evaluations and research. Learning whether and how an eHealth solution could fit within the local context provides key information for local decision making. If the design of an eHealth trial fails to create conditions for professionals to gain experiences, it might prevent learning and obtaining evidence that are crucial to increase the success rate of eHealth trials in their post-pilot phase and reduce “pilotism” ,20. MoreTo support the use of evaluation evidence, one could consider assessing a few, rather than a large number of, variables during an evaluation. It can help ensure the quality of data collected, preventing from making trade-offs between the time required and quality of the data. Additionally, shorter iterative cycles of evaluation could create better possibilities for health care professionals to gain more experience and use it as evidence in decision making regarding integration of eHealth solutions. Professional experiences could enhance evidence when relevant professionals are included and accumulate experience during eHealth trials. Such an approach could increase the degree of learning and chances that the eHealth solution would be integrated into practice and reach sustainability. Existing scientific evidence collected from previously conducted projects or initiatives in the same location as an eHealth solution is implemented can support decisions regarding eHealth integrations better than scientific evidence obtained from other locations. Research evidence produced in other contexts can be problematic for direct translation into making such decisions. However, it has value for scientific dissemination.The findings of this study were based on a specific innovation research project funded by the European Union. The interviewee sample was delimited to the partners of the project, since they had deep knowledge of the project and its results and were in the favorable position to use the evidence obtained. However, perspectives of the funding agency, industry, or governments could be a valuable avenue for further research. Similarly, evidence produced in other settings and study designs could provide a different view of the use of evidence. Furthermore, the study captured the situation 8 months after the Alpha project was finished. However, the use of evidence might be more extensive in later stages due to the so-called “gestation period” .Previous research on evidence-based practice usually described clinical implementations and policymaking ,7 as eviOur study shows that the typical discourse on the instrumental use (in making decisions) of evidence (and lack of it)  does notOur study indicates that experiences in eHealth trials matter more than facts when it comes to how evidence is used for eHealth integration. The reason for this could be that “hard facts” from evaluation are difficult to straightforwardly implement in a complex health care reality ,27. AlthWe identified a number of strategies to support the use of evidence in practice in the context of eHealth trials. First, we suggest focusing on a smaller number of variables during evaluation, to ensure the quality of collected data. Such a strategy is contradictory to the ever-expanding evaluation frameworks that include a growing number of variables eg, ,32) and  and 32])We conclude that various stakeholders  benefit from evidence differently. Therefore, the delimited focus of research on decision making in clinical settings or policymaking does not capture the actual beneficiaries and realities of evidence use. In addition, when making decisions regarding improving practice, stakeholders do not necessarily rely on the factual evidence produced by evaluations. We conclude that, in the context of eHealth, professional experiences seem to have more influence over decisions than formal evaluations and research. Hence, we suggest that scientific and practical discussions around evidence-based practice in the context of eHealth should include all sorts of evidence . Additionally, it could be beneficial to have an in-depth view on how the evidence is used. This could help expand the conventional focus on clinical settings or policymaking and the direct use of evidence produced by research or evaluation when making decisions. Initiatives to support and standardize evidence-based practice in the context of eHealth should take these complexities into consideration to achieve better uptake of evidence in practice.


Graphic display formats are often used to enhance health information. Yet limited attention has been paid to graph literacy in people of lower education and lower socioeconomic status (SES). This study aimed to: 1) examine the relationship between graph literacy, numeracy, health literacy and sociodemographic characteristics in a Medicaid-eligible population 2) determine the impact of graph literacy on comprehension and preference for different visual formats.We conducted a cross-sectional online survey among people in the US on Medicaid, and of presumed lower education and SES.The mean graph literacy score among 436 participants was 1.47 . Only graph literacy was significantly associated with overall comprehension (p < .001). Mean comprehension scores were highest for the table format (1.91), closely followed by bar graph (1.85) and icon array (1.80). Information comprehension was aligned with preference scores.Graph literacy in a Medicaid-eligible population was lower than previous estimates in the US. Tables were better understood, with icon arrays yielding the lowest score. Preferences aligned with comprehension.It may be necessary to reconsider the use of graphic display formats when designing information for people with lower educational levels. Further research is needed. Understanding health information and related numerical information is critical to making informed decisions, promoting adherence to treatment, and improving health outcomes –8. It isth century ..31].We used self-reported current Medicaid enrollment as a proxy measure of lower SES to recruit people of presumed lower SES and lower educational attainment living in the United States (US) (determined using panel IDs). Medicaid is a joint federal and state program that provides health coverage to over 72 million people in the US. Medicaid enrollees include low-income families, qualified pregnant women and children, individual with disabilities and individuals receiving Supplemental Security Income (SSI). We asked the Qualtrics team to target the recruitment of Medicaid enrollees on their panels. In addition, we used three screening questions aiming to only include participants who reporting being at least 18 years old, being a Medicaid enrollee, and felt comfortable reading and completing a survey in English. We based the sample size on the estimated number of adults in the US enrolled in Medicaid and using a 95% confidence interval with a 5% margin of error. We identified unique visitors using unique panel IDs and checked for duplicates. Qualtrics does not use IP addresses to check for duplicates. In addition, cookies were placed upon entering the survey to prevent participants from completing the survey more than once.www.qualtrics.com) is a customer experience company and online survey platform that sends online surveys to a targeted population of respondents. A survey invitation containing a link was sent out to them based on the profiling data provided to Qualtrics (described above). To encourage participation, the participants were offered points that they could redeem for prizes.The survey was exclusively open to Qualtrics’ active panel participants. Qualtrics  over the age of 18, 2) currently enrolled in Medicaid, and 3) comfortable completing the survey in English. Participants were subsequently asked to provide demographic information (six questions) and answer the one-item health literacy question. The next two pages tested graph literacy (four questions) and subjective numeracy (three questions). The following page provided a short introduction to the comprehension questions. The subsequent three pages each assessed comprehension of fictitious risk information using icon arrays, bar graph, and table (three questions each). We purposefully chose not to use pie charts as previous studies have demonstrated that this format leads to slower and less accurate responses , 32. TheWe measured health literacy using Chew’s one-item scale, “How confident are you in filling out medical forms by yourself?” The answer choices were extremely, quite a bit, somewhat, a little bit, and not at all . Based oWe measured subjective numeracy using the validated 3-item Subjective Numeracy Scale (SNS3), which asks respondents to rate their perceived ability in using numbers  34]. Th. Th34]. To minimize burden on respondents, we used the validated 4-item short version of the graph literacy scale . The graParticipants viewed hypothetical recurrence risks of cancer treatments in three different formats: table, bar graph, and icon array. They answered three comprehension questions following each visual display format. The first question required basic interpretation of the recurrence risk after one treatment option (gist task); the second required expressing the recurrence risk out of 100, 10 years after a given treatment (verbatim task), and the third question required comparing the difference in recurrence risk between two options (verbatim task). Each correct answer was given a point of 1, each incorrect answer was given a score of 0. Answers to free text questions were dual coded with conflicts discussed with a third coder. The comprehension score for each format equaled the number of first three questions answered correctly (range 0–3). We also examined the total comprehension score, which was the sum of the scores for all formats (range 0–9).For each visual display format , we asked about overall preference for visual display format.The survey was developed in Qualtrics using the validated scales described above. Other items (such as the comprehension questions) were developed by the research team, based on existing literature in this area, and tested with a convenience sample of research collaborators and lay users. Testing focused on the usability and readability of the written content of the survey and visual display formats embedded in the survey. Only minor formatting and typographical edits were made.We performed simple logistic regressions to assess the unadjusted relationship between sociodemographic factors including gender, education, having one or more chronic conditions, and having family history of cancer and the dichotomous variables for health literacy, SNS3 and graph literacy. We also performed a multiple logistic regression with graph literacy as the dependent variable and all sociodemographic variables, SNS3, and health literacy as independent variables to assess how each independent variable was associated with graph literacy.We performed one-sample t-tests to determine if there were differences in comprehension scores by format. We performed simple logistic regressions to assess the unadjusted relationship between graph literacy and overall comprehension and graph literacy and preference. We performed a multiple logistic regression to evaluate whether the relationship between graph literacy and comprehension changed when controlling for preference, SNS3, health literacy, age, gender, education, having one or more chronic conditions, and having family history of cancer with graph literacy as the dependent variable and all other factors as independent variables.We performed simple linear regression to assess the relationship between preference and overall comprehension score. We performed multiple linear regression to assess this relationship while controlling for SNS3, health literacy, age, gender, education, having chronic conditions, and having family history of cancer.We performed a simple linear regression to assess if overall comprehension score was different based on which graph format was seen first. We also looked at the individual format scores based on format order using simple logistic regressions to determine if individual format scores varied based on which format was seen first. A logistic regression was used since we dichotomized the variable of icons seen first (yes/no as dependent variable).The study was approved by the Committee for the Protection of Human Subjects (CPHS) at Dartmouth College on August 7, 2017.Approximately 6,905 individuals on the Qualtrics panel received the invitation, of whom 1,393 accessed the survey (20.2%). After giving consent to participate, 932 participants answered “no” to at least one of the three screening questions (including receiving Medicaid) and were excluded (66.9%). Of those who answered “yes” to all three screening questions (n = 461), 25 did not finish the survey . Of thosAll participants self-reported being Medicaid enrollees. The majority of participants were female (82.3%) and the mean age was 40.5 years. Most participants identified as White or Caucasian (68.8%), followed by Black or African American (19.7%) and Spanish or Latino/a (8.7%). The vast majority (85.8%) did not have a bachelor’s degree, 41.5% reported having a high school diploma or less and 44.3% reported having some college education or an associate degree. See For brevity, we only report odds ratios and 95% confidence intervals where there was statistical significance. See A total of 374 participants (85.8%) reported adequate health literacy. There was no statistically significant relationship between health literacy and gender (p = .73) or having at least one chronic condition (p = .75). Respondents with adequate health literacy were more likely to have higher education  and older . Respondents with a family history of cancer had lower odds of adequate health literacy .The mean SNS3 score was 11.1 where a higher score indicated higher subjective numeracy . When dichotomized, 48.9% of participants (213/436) were in the high numeracy group and 51.2% (223/436) were in the low numeracy group. The was no statistically significant relationship between SNS3 and a family history of cancer (p = .56) or having chronic conditions (p = .57). Women were less likely than men to have high numeracy . Respondents with a bachelor’s degree or higher had greater odds of high numeracy .The mean graph literacy score was 1.47  where a higher score indicates higher graph literacy. When dichotomized, 52.1% of participants (227/436) were in the low graph literacy group and 47.9% (209/436) were in the high graph literacy group. The relationship between graph literacy and gender (p = .22), having chronic conditions (p = .27), having a family history of cancer (p = .10), or higher education (p = .73) was not statistically significant.In the bivariate analysis, the relationship between graph literacy and health literacy was not statistically significant (p = .31). Respondents with higher SNS3 had higher odds of adequate health literacy . Respondents with higher graph literacy had greater odds of higher SNS3 . In the multiple logistic regression, graph literacy was not associated with any independent variable.Mean comprehension score by format was highest for table (1.91), then bar graph (1.85), followed by icon array (1.80) . Mean overall comprehension score was 5.6 (SD = 2.5). The majority of the sample preferred tables or bar graphs (39.2% and 38.1% respectively) .In the simple logistic regressions, higher overall comprehension was associated with higher graph literacy  and preference for icons was associated with lower graph literacy compared to preference for tables . In the multiple logistic regression controling for preference, SNS3 score, age, education, history of chronic disease, family history of cancer, and health literacy, graph literacy was still strongly associated with overall comprehension .In the simple linear regression, preference for bars or icon arrays was associated with lower overall comprehension scores when compared to preference for tables . In the multiple linear regression, preference for bars or icon arrays was still associated with lower overall comprehension scores when compared to preference for tables .When looking at order effects, we found that participants who saw bar graphs or icon arrays first had lower overall comprehension scores when compared to seeing tables first . If participants saw icon arrays first they had a lower icon array score than if they saw the tables or bar charts first . No other order effects were significant.We found a positive relationship between graph literacy and numeracy. Numeracy was related to gender, education, and health literacy. Only graph literacy was significantly associated with the total graph comprehension score. There was alignment between comprehension scores and preferred visual display format, with tables being preferred and yielding the highest comprehension score. Icon arrays yielded the lowest comprehension score and were least preferred. Regarding order effects, participants who saw bar graphs or icon array first had lower overall comprehension scores.This study was the first online survey of graph literacy in a Medicaid eligible population with lower education conducted in the US. The characteristics of our sample were broadly representative of the Medicaid enrolled population. A major strength is the inclusion of participants on Medicaid, with lower education and lower graph literacy. Most study limitations are related to the nature of online survey data collection and the highly selected nature of the study sample. The measures used have not been fully validated for online use although most have previously been used with online samples. The response rate was low when considering the large number of people who received the online survey invitation . However, of those who accessed the survey and were eligible , 94.6% completed the survey. Our sample was derived from an online panel of respondents  and may not be fully representative of the US population receiving Medicaid, with lower education, and presumed lower SES. Social desirability bias could have affected the responses participants provided, particularly on the subjective health literacy  and numeracy scales. This bias may have led some participants to respond to those questions based on social expectations, particularly for participants with higher education levels. The survey response rate of 20.2% is aligned with average online survey responses rates in online panels , 39.The mean graph literacy score was considerably lower  than mean scores reported in the validation of the short form graph literacy scale in a sample of the US  and German populations . This may indicate that people of lower educational attainment and lower SES have considerably lower graph literacy than the general population and may not always benefit from information presented graphically. This hypothesis may also be supported by the fact that 39.2% of our sample reported preferring using the table to process numeric health information, while seemingly understanding information better in this format.Nayak’s study of graph literacy in a highly educated sample of prostate cancer patients assessed graph literacy using the 13-item version of the scale . It is tMean numeracy scores in our sample of survey respondents with lower educational attainment were considerably lower than numeracy scores found in Nayak’s highly educated sample, and McNaughton’s study , 44. SevIn our sample, preference for a visual display format coincided with better comprehension. This finding is not consistent with prior studies , 40. In In light of our findings, we may want to investigate those issues further and possibly reconsider the use of graphic display formats among people of lower graph numeracy, who are more likely to hace lower education and lower numeracy. Further research that compares visual display formats among people of lower SES recruited in community settings  may be warranted. Offering multiple ways to present and process numeric health information, including numbers alone using natural frequencies as well as bar charts (given the stated preference) may be worth exploring . Should Despite frequently reporting adequate health literacy, graph literacy among a Medicaid eligible population was considerably lower than the mean scores previously collected in the general population in the US. Our study findings suggest that in people of lower educational attainment, graph literacy seems to be the strongest predictor of graph comprehension and is correlated with numeracy. Further, preference for a particular display format mirrored comprehension on this format. Both tables and bar charts lead to slightly higher comprehension scores than icon arrays, and are thus understood differently by different people. It may therefore be necessary to reconsider the use of graphic display formats when designing information for people with lower educational levels. Given the stated limitations of the present survey, further research in this area is needed.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.


Numerous instruments are designed to measure digital literacy among the general population. However, few studies have assessed the use and appropriateness of these measurements for older populations.This systematic review aims to identify and critically appraise studies assessing digital literacy among older adults and to evaluate how digital literacy instruments used in existing studies address the elements of age-appropriate digital literacy using the European Commission’s Digital Competence (DigComp) Framework.Electronic databases were searched for studies using validated instruments to assess digital literacy among older adults. The quality of all included studies was evaluated using the Crowe Critical Appraisal Tool (CCAT). Instruments were assessed according to their ability to incorporate the competence areas of digital literacy as defined by the DigComp Framework: (1) information and data literacy, (2) communication and collaboration, (3) digital content creation, (4) safety, and (5) problem-solving ability, or attitudes toward information and communication technology use.Searches yielded 1561 studies, of which 27 studies  were included in the final analysis. Studies were conducted in the United States (18/27), Germany (3/27), China (1/27), Italy (1/27), Sweden (1/27), Canada (1/27), Iran (1/27), and Bangladesh (1/27). Studies mostly defined older adults as aged ≥50 years (10/27) or ≥60 years (8/27). Overall, the eHealth Literacy Scale (eHEALS) was the most frequently used instrument measuring digital literacy among older adults . Scores on the CCAT ranged from 34  to 40 . Most instruments measured 1 or 2 of the DigComp Framework’s elements, but the Mobile Device Proficiency Questionnaire (MDPQ) measured all 5 elements, including “digital content creation” and “safety.”The current digital literacy assessment instruments targeting older adults have both strengths and weaknesses, relative to their study design, administration method, and ease of use. Certain instrument modalities like the MDPQ are more generalizable and inclusive and thus, favorable for measuring the digital literacy of older adults. More studies focusing on the suitability of such instruments for older populations are warranted, especially for areas like “digital content creation” and “safety” that currently lack assessment. Evidence-based discussions regarding the implications of digitalization for the treatment of older adults and how health care professionals may benefit from this phenomenon are encouraged. Adopting digital technology is becoming imperative for all areas of service and business including health care. In the era of global aging, digital technology is viewed as a new opportunity to overcome various challenges associated with aging, such as reduced physical and cognitive function, multiple chronic conditions, and altered social networking . ConsistImproving the inclusion and engagement of older adults in digital technology is becoming increasingly important for the promotion of their health and function . While nFor health care professionals, the rapid digitalization of social and health care services has various implications for providing older adults with improved access, knowledge, and behavior . TeleheaQuantifying the digital literacy of older adults is the first step to assist older adults to take advantage of this trend of digitalization in health care. However, when measuring digital literacy among older adults, measures must consider how basic competencies among one age cohort can be harder to achieve for another cohort with fewer information-and-communication-technology experiences and opportunities . In the To our knowledge, few systematic reviews to date have evaluated instruments of digital literacy for older adults in general, although 1 systematic review of digitally underserved populations attributed poor eHealth literacy to age, as well as language, educational attainment, residential area, and race . FurtherTherefore, this systematic review aimed to (1) identify and critically appraise studies that involved the assessment of digital literacy among older adults and (2) evaluate how digital literacy instruments used in existing studies address the elements of age-appropriate digital literacy using the European Commission’s Digital Competence (DigComp) Framework . AccordiThis systematic review was conducted by searching multiple electronic databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . ElectroThe reference lists of identified studies were manually reviewed by a team of academics to prevent relevant studies from being excluded in our search for relevant articles. EndNote X9 was used for database management.We included studies that (1) were published in English, (2) targeted older adults, and (3) measured the use of a validated instrument to assess digital literacy. However, publications were excluded if older adults were not the study’s main target population. To elaborate, publications targeting the general population, for example, were excluded from our list of eligible articles as older adults were not the main target population examined.Exceptions to this rule were studies that compared older populations to younger populations with the aim of addressing the age-related digital divide, like the study by Schneider and colleagues  comparinUsing these eligibility criteria, 3 independent investigators  examined all studies reporting the use of a digital literacy instrument in the databases and search engines. All studies were screened according to their title and excluded if the main target population did not consist of older adults.Subsequently, abstracts were screened so that non-English studies and studies not assessing digital literacy through a validated instrument could be excluded from our investigation. During this process, any studies that were incapable of providing information on the required general characteristics were excluded.Last, full-text reviews were performed to ensure that all articles measured the digital literacy of older adults through validated instruments. In this process, investigator-developed questionnaires were included only if authors mentioned that they had been evaluated by experts for face validity. The instruments mentioned in each article were checked to ensure that they were accessible for our quality assessment. All processes were supervised by 2 independent reviewers (SC and JC), and any disagreement was resolved through discussions.Data on the general characteristics of the included studies included a summary of the year of publication, study design, region where the study was conducted, age of older adults studied, and main literacy instrument used. Regarding the region where the study was conducted, 2 studies were international collaborations, including 1 study between Italy and Sweden  and anotThree independent reviewers  assessed the quality of each included study using the Crowe Critical Appraisal Tool (CCAT) . The CCAInstruments were also assessed to the DigComp’s definition of the 5 areas of digital literacy: (1) information and data literacy , (2) communication and collaboration , (3) digital content creation , (4) safety , and (5) problem solving  .The PRISMA flow diagram in Of the 78 articles assessed for eligibility, 50 were excluded for the following reasons: (1) no report of an instrument for digital literacy despite the title or abstract of the paper alluding to measures of digital literacy (n=23); (2) the instrument presented was not validated (n=13); (3) studies were mainly on population assessments and measured digital literacy only as part of a wider assessment of multiple factors (n=10); (3) instruments were not available in English or in a publicly accessible format (n=4); and (4) the study did not specifically target older adults (n=1). Ultimately, 27 articles were included in our review.As seen in In this systematic review, we highlighted the importance of digital literacy among older adults and provided a comprehensive overview of the instruments that are being employed to measure their digital literacy. We also illustrated the various strengths and limitations of each instrument, relative to age-appropriateness and suitability for older adults, in accordance with the components of a validated, digital competency framework . Our revIn the digital era, providing education for patients regarding management of their physical or mental illness or injury, explaining posttreatment home care needs, and managing their diet, nutrition, and exercise are all duties that are beginning to be “digitalized” . MoreoveOverall, we discovered that while the eHEALS is most frequently used to measure digital literacy among older adults, the MDPQ may be more appropriate for measuring the literacy of older adults. Unlike the eHEALS, the MDPQ attempts to measure older adults’ digital content creation capacity , which according to the European Commission, can give valuable information regarding an individual’s ability to add value to new media for self-expression and knowledge creation .Also, the MDPQ contains numerous items related to data protection and privacy such as “passwords can be created to block/unblock a mobile device” or “search history and temporary files can be deleted” despite the fact that security was the least measured element of the DigComp Framework among the instruments in our study. Only the CAS, eHEALS, and MDPQ provide items related to data protection and privacy, which is concerning given that older adults comprise a significant proportion of the target population for internet scams or email attacks .In our review of 27 selected articles, more than half  used the eHEALS to measure the digital literacy of older adults. Several reasons can be speculated; this instrument is short (8 items), and the questions are simple to understand . Scholars claim that it is easy to administer to older adults . It shouStudies focusing on similar research aims also employ similar instruments. For example, the CAS was used in 2 studies that focused on computer anxiety and confidence. In the existing body of literature, the CAS has often been used for studies targeting individuals in highly stressful environments such as business graduate students , psychiaRegarding the overall quality of the included studies evaluated using the CCAT, some variation existed among the studies reviewed. Studies that were cross-sectional or lacked acquisition of written informed consent and used alternate approaches, such as telephone or self-reported, web-based or email surveys, scored poorly in the “design” and “ethical matters” category. Studies also lost marks if there was no flow diagram, there was no mention of design methods in the title of their manuscript, or they had biased sampling methods .Contrastingly, 2 RCTs in our review received a score of 100% on the CCAT, as they had excellent preliminaries, introductions, study design, sampling methods, data collection methods, ethical matters, results, and discussions. These studies employed performance-based measures like the Attitudes Toward Computer/Internet Questionnaire  and UTAUT model (face-to-face interview), which are more reliable data collection methods than self-administered questionnaires. Performance-based measures like these may be suitable for studies targeting older adults, but it should be noted that clinical environments and personal fitness can greatly influence outcomes, especially if environments contain learners of mixed ability , rapid pAs aforementioned, for older adults, it is important that the research and design of digital technologies encompass the heterogeneity of their capacity. While we believe that instructions should be “clear and understandable” to study participants , we alsoPrevious studies recommend that, instead of employing the full MDPQ or technology acceptance model (TAM), the shorter, 16-question version , or seniUltimately, many limitations exist in our review. First, it is important to mention that the association between age and digital capacity is controversial among certain scholars who argue that age-based divisions are too simplistic  and unclLikewise, digital literacy is a process-oriented skill, and measuring it in isolation may be inaccurate for quantifying an individual’s skillset . In the Also, as observed in our review, many of the investigated instruments, including the Attitudes Toward Technologies Questionnaire, TAM, ATC-IQ, APOI, and CAS, measured attitudes or perceptions toward technology rather than digital aptitude itself. While studies on attitude are important, the lack of measures examining older adults’ abilities to use information and communications technology was an unexpected limitation of the reviews studied.Last, even though previous studies have argued that the DigComp Framework is one of the broadest and most generalizable frameworks for assessing digital literacy measures ,21, it iIn conclusion, more studies are required so that the measurement of digital literacy among older adults can become more elaborate and specific. Digital literacy evidently has strong associations with the utility of information and communications technologies that promote physical and mental well-being among older adults. Further assessments and studies of digital literacy among older adults that overcome the limitations of existing research and measurement designs would allow for better allocation of support and resources to address the diverse health care needs of this growing but vulnerable population.


Health literacy is becoming increasingly important in areas such as cancer care, where treatments are relatively difficult to navigate. This study aims to describe the how health literacy is associated with healthcare outcomes and health system usage among patients with lung cancer. Data include retrospective medical record data from 456 patients with lung cancer; half were age 70 and older. Patients were coded as having adequate or limited health literacy based on their response to their Single Item Literacy Screener (SILS). Data were collected from a 12 month period following diagnosis for each patient. One-third of patients had limited health literacy; this was significantly more common among adults age 70 and older. Patients with limited health literacy were more likely to have newly diagnosed lung cancers of stage 3B or higher  compared to those with adequate health literacy. Patients with limited health literacy had higher median levels of depression based on the PHQ-9 questionnaire  and a higher median number of emergency department visits  and unplanned hospitalizations . Furthermore, patients with limited health literacy were more likely to have an emergency department visit or unplanned hospitalization sooner (p < 0.0001). These data illustrate a lower quality of life and a higher dependency on healthcare services for patients with limited health literacy. Assessment and interventions may be necessary to ensure access to quality healthcare.