Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study

Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study Martin Sollie, Marianne Hansen, Jørn Bo Thomsen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2982014/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Information technologies are increasingly used when informing patients about their disease, treatment and prognosis. These digital platforms have many advantages compared to traditional education interventions. There are concerns that some patients may have difficulty with this mode of information delivery. This warrants the question; are our patients ready for the transition towards more digital information technologies? We aimed to assess health technology readiness profiles amongst women with a suspected breast cancer diagnosis. Secondly, we wanted to investigate the potential differences between these profiles according to sociodemographic factors and the patients´ current use of technology. Methods This cross-sectional study used the Readiness and Enablement Index for Health Technology (READHY) questionnaire. We included all patients (n=92) referred with suspected breast cancer. Results The cluster analyses identified three distinct profiles. Patients in profile 1 (n=54) demonstrated medium health technology readiness. Profile 2 (n=18) reported high scores on all parameters. Profile 3 (n=20) scored lowest on all parameters indicating problems with health literacy, eHealth literacy and insight into their health. Profile 3 also reported higher levels of emotional stress. Conclusions Our study found that most patients had medium to high health technology readiness, but we also identified a group with lower health technology readiness. Based on our results, healthcare personnel dealing with women with suspected breast cancer should be aware of patients struggling with health technology. Age and technology familiarity may indicate vulnerable patients. More studies investigating different modes of information delivery with follow-ups are needed. Breast Cancer Health Technology Readiness Health Technology Figures Figure 1 Figure 2 Introduction Being diagnosed with breast cancer can negatively impact mental health, with studies showing higher anxiety levels, depressive symptoms, distress and trauma-related symptoms[1]. Following initial diagnosis, the patients often undergo further diagnostics and treatment[2]. These steps are usually initiated and performed as fast as possible to avoid delay in treatment. In close cooperation with the healthcare personnel, the patients make treatment decisions. Patients must receive relevant and comprehensive information to enable them to choose their preferred treatment. Previously this information was mainly given orally in consultations with healthcare personnel or using printed materials. Now, in the technology era, the use of digital solutions for patient information is rapidly increasing, and a growing body of evidence supports the use of digital health technology[3, 4]. These platforms have many advantages, such as being more time- and cost-effective than traditional education interventions. It also enables us to provide relevant information uniformly to all our patients[5]. Though the use of digital information is increasing, we have yet to thoroughly investigate whether patients are ready for this change. One way to evaluate the health technology readiness of our patients is the newly developed Multidimensional Readiness and Enablement Index for Health Technology (READHY) questionnaire[6]. The tool assesses the many aspects of health technology readiness, such as technology skills, emotional distress, social support and ability to engage with digital services. It can also identify subpopulations within patient groups with problems using and understanding health technology[6]. There is a gap in the published literature on health technology readiness among women with newly diagnosed or suspected breast cancer. Therefore, we have designed a prospective study using the READHY tool. The aims of this study are (1) to identify health technology readiness profiles amongst women with a suspected breast cancer diagnosis. (2) to investigate the differences between these profiles according to sociodemographic factors and their current use of technology. Methods We designed this study as a cross-sectional study using the READHY questionnaire. It was registered at ClinicalTrials.gov before initiation. Identifier: NCT04745117[7]. The study was reported per The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement[8]. Setting and participants The study was conducted at the Department of Plastic Surgery at Odense University Hospital. The participants were constituted by a convenience sample obtained from 01.03.2021 through 31.12.2021. All patients referred to us with suspected breast cancer were asked to participate. Our department receives patients from the area of Funen and the surrounding islands. The exclusion criteria were: not being able to understand and read Danish. Patients were referred via the official screening program for breast cancer in Denmark, where all women between 50 - and 69 years are offered a mammogram every two years. If the clinical mammogram raised suspicion of breast cancer, they were referred to us as the closest breast surgery centre. Additionally, patients with clinical suspicion of breast cancer were referred by general practitioners. We asked the patients to complete the questionnaire on their first or second visit to our department. The patients answered the questionnaires electronically using a tablet. If the patients wanted to fill out the questionnaires on paper, this was arranged by the attending nurse. The patients were informed orally and in writing about the study before giving consent to participate in the study. The questionnaire constituted a section of demographic data and the READHY tool. We collected the following demographic data: gender, age, the highest level of education, cohabitation status, and source of income. We also asked the participants if they owned a smartwatch, tablet, or computer and their main purpose when using information technology (IT). The Readiness and Enablement Index for Health Technology (READHY) The READHY tool was used to assess readiness for health technology. The tool consists of the eHealth Literacy Questionnaire (eHLQ)[9], which includes seven scales, supplemented with four scales from the Health Education Impact Questionnaire (heiQ)[10] and two scales from the Health Literacy Questionnaire (HLQ)[11]. Together, these scales capture eHealth literacy, self-management, and social context. The READHY tool is a validated assessment tool[6]. The 13 scales were assessed using 65 items. Each item was presented to the participant as a statement and scored on a 4-point rating scale, ranging from 1=strongly disagree to 4=strongly agree. The overall score of each scale was calculated as the mean score of the 4–6 items (i.e., statements) that constitute the scale. If less than 50% of items in a scale were answered, the scale was regarded as missing; in those cases, the survey was considered incomplete. Statistical methods We consulted a clinical statistician before initiating the study. Based on previously published data using the READHY tool, we were advised to aim for approximately one hundred participants. Our data-analysis method was based on a previously published study with a similar aim by Thorsen et al. [12]. We used a data-driven approach with a combination of hierarchical and K-means cluster analysis to divide participants into clusters depending on their level of readiness for health technology. We have chosen to refer to these clusters as profiles. We used the two-step k-means analysis and Bonferroni post hoc analyses using the one-way analysis of variance (ANOVA) to decide the optimal number of clusters. The results assessed three profiles as the best fit and two as the second best fit. We conducted K-means cluster analyses for three profiles in 8 iterations. The difference between profiles for each ready scale was assessed using a one-way ANOVA. For sociodemographic and IT-related data, the differences between the profiles were tested using the Fisher exact test for frequencies and the one-way ANOVA for continuous variables. Frequencies are reported as numbers and proportions, and continuous variables are reported as mean and standard deviation (SD). Data were analysed as observed. No imputations were used to replace missing data. Data were analysed using SPSS version 28[13]. The statistical analyses were performed under the guidance of a clinical statistician. Results Readiness for Health Technology The cluster analyses of the total READHY scores resulted in three distinct profiles of participants. They are presented in Figures 1 and 2. The different profiles scored high, medium and low in their overall READHY score. Profile 2 (n=18) consistently scored high on all scales, while profile 3 (20) revealed low scores. Profile 1 (n=54) consistently scored medium on all scales. 60% of participants were allocated to profile 1. The other two profiles, 2 and 3, comprised 20% of the participants each. Profile 3 scored lowest on all parameters when assessing the READHY items regarding health literacy, the eHLQ and HLQ, indicating that these patients felt less supported by their healthcare providers and social network. They also reported low motivation to engage with digital services and insufficient access to digital services that suit their needs. The same pattern was seen in the heiQ, where profile 3 also consistently scores the lowest, indicating problems with self-monitoring and insight, constructive attitudes and approaches to health education. They also reported a higher emotional stress level than the other two profiles. [Figure 1] [Figure 2] Sociodemographic Characteristics The sociodemographic characteristics of the total population and the individual profiles are presented in Table 1. When comparing the different profiles, we observed that participants in profile 2, with the highest scores, were younger (57.9 y) than the participants in profiles 1 (62.5 y) and 3 (69.5 y) (P=0.013). We did not detect any statistically significant differences in the groups regarding their highest level of education, cohabitation status or source of income. Profile 3 had the highest portion of patients, with only comprehensive school as their highest level of education. Yet, the frequency differences were not statistically significantly different from the other two profiles. Regarding cohabitation status, there was a tendency for patients in profile 3 more often lived alone compared to the other two groups, but no statistically significant difference was detected. Patients in profile 3 had the lowest frequency of salary as income, with a higher portion receiving retirement pension or public income support. [Table 1] Table 1: Sociodemographic characteristics and access to technology of participants (N=92) across the identified profiles. Data are presented as mean (SD) for continuous variables and number (proportions) for frequencies. Characterstics Total (N=92) Profile 1 (N=54) Profile 2 (N=18) Profile 3 (N=20) P-value Age, mean (SD) 63.1 (12.4) 62.5 (11.9) 57.9 (12.7) c 69.5 (11.1) b 0.013 Highest attained level of education, n (%) 0.277 Comprehensive school 16 (17.4) 8 (14.8) 1 (5.6) 7 (35.0) Short education (2-3 y) 40 (43.5) 25 (46.3) 8 (44.4) 7 (35.0) Medium education (3-4 y) 34 (37.0) 20 (37.0) 8 (44.4) 6 (30.0) Long education (4+ y) 2 (2.1) 1 (1.9) 1 (5.6) 0 (0.0) Cohabitation status, n (%) 0.451 Living alone 23 (25.0) 13 (24.1) 3 (16.7) 7 (35.0) Living with spouse and/or children 69 (69.0) 41 (75.9) 15 (83.3) 13 (65.0) Source of income 0.650 Salary 38 (41.3) 25 (46.3) 10 (55.6) 3 (15.0) Retirement pension 11 (11.9) 6 (11.1) 1 (5.6) 4 (20.0) Public income support/ no incomes 43 (46.7) 23 (42.6) 7 (38.8) 13 (65.0) Do you own any of these IT aids? Smartwatch? 0.845 YES 13 (14.3) 8 (14.8) 3 (16.7) 2 (10.0) NO 79 (85.8) 46 (85.2) 15 (83.3) 18 (90.0) Smartphone? 0.006 YES 85 (92.3) 53 (98.1) c 17 (94.4) 15 (75.0) a NO 7 (7.6) 1 (1.9) c 1 (5.6) 5 (25.0) a Computer? 0.130 YES 77 (83.7) 46 (85.2) 17 (94.4) 14 (70.0) NO 15 (16.3) 8 (14.8) 1 (5.6) 6 (30.0) Tablet? 0.030 YES 64 (69.5) 40 (74.1) c 16 (88.8) c 8 (40.0) a,b NO 28 (30.4) 14 (25.9) c 2 (11.1) c 12 (60.0) a,b How do you use technology in your daily life? Excersize? 0.006 YES 17 (18.5) 8 (14.8) b 8 (44.4) a,c 1 (5.0) b NO 75 (8,1) 46 (85.2) b 10 (55.5) a,c 19 (95.0) b Work? 0.005 YES 38 (41.3) 23 (42.6) c 12 (66.7) c 3 (15.0) a,b NO 54 (58.7) 31 (57.4) c 6 (33.3) c 17 (85.0) a,b Seeking information? 0.009 YES 76 (82,6) 47 (92.2) c 17 (94.4) c 12 (60.0) a,b NO 16 (17.4) 7 (13.7) c 1 (5.6) c 8 (40.0) a,b Communication? 0.496 YES 77 (83.7) 46 (85.2) 16 (88.9) 15 (75.0) NO 15 (16.3) 8 (14.8) 2 (11.1) 5 (25.0) Entertainment? <0.001 YES 62 (67.4) 39 (72.2) c 17 (94.4) c 6 (30.0) a,b NO 30 (32.6) 15 (27.8) c 1 (5.6) c 14 (70.0) a,b a) Different than profile 1 b) Different than profile 2 c) Different than profile 3 IT use The reported IT use is presented in Table 1. The participants in the lowest scoring, profile 3, had a statistically significant lower percentage of smartphone ownership (75%) compared to profiles 1 (98%) and 2 (94%) (p=0.006). The same was observed regarding tablet ownership, with a rate of 40% in profile 3 and 74% and 89% in profiles 1 and 2, respectively (p=0.03). We did not detect any significant difference in computer and smartwatch ownership frequencies. When asked about their use of IT in daily life, the youngest group, profile 2, had the highest use of technology during fitness and exercise (15%), compared to the two other profiles, 1 (15%) and 3 (5%). The difference was statistically significant across the profiles (p=0.006). Similar results were observed for the variable “IT in work situations”. When asked about their use of IT in seeking information, almost all participants in profiles 1 and 2 stated yes, while the frequency was much lower in profile 3 (92.2%, 94,4% and 60.0%, respectively. p=0.009). For communication purposes, there was no statistically significant difference across the groups. Profile 2 used the most IT for entertainment purposes (94.4%), while profile 1 (72.2%) and profile 3 (30.0%) stated much lower usage (P=<0.001). Discussion This is the first study investigating the health technology readiness amongst patients referred to the hospital with suspected breast cancer using the READHY tool. Most patients, around 80%, demonstrated medium to high levels of health technology readiness. Our study's main finding is identifying patients with lower READHY scores in all the measured parameters. This group comprised around 20% of the total participants in our sample. This group had the lowest scores on the Health Education Impact Questionnaire (heiQ), indicating a higher level of emotional stress, less self-monitoring and health insight, and a lower skill set to manage their own health. The same pattern was seen in the eHealth Literacy Questionnaire (HLQ), with more issues related to locating, using, and understanding digital healthcare information. They also scored the lowest on The Health Literacy Questionnaire (HLQ) when asked about the support system surrounding their health, their ability to take action regarding their healthcare, and their understanding of information about their health. This is in line with other studies identifying subpopulations of lower health technology readiness in other patient groups, such as diabetes type II. Factors associated with these subpopulations were, amongst others, age, emotional well-being, familiarity with IT and degree of eHealth literacy[ 12 ]. We investigated sociodemographic factors, and IT use to find some factors that could help us locate these potentially more vulnerable patients. The only sociodemographic factor related to profile 3 was age, as these patients were statistically significantly older than the other two. We also observed that patients in profile 3 were less likely to be familiar with the use of technology regarding exercise, seeking information and entertainment purposes. This is not surprising as previous studies have found that older patients may not be able to or wish to engage with electronic health resources[ 14 , 15 ]. The main limitation of this study is the cross-sectional design, with a lack of follow-up. We have no data on how the patients eventually received and understood the information given to them. We did not register the diagnostics journey of our patient before being referred to our department, and there is a risk that being referred from the screening program or via t the general practitioner could influence the READHY results, especially regarding the items of emotional distress. Our convenience sample constituted only ninety-two patients, which could be low when making a cluster analysis. We do, however, feel that the results clearly indicate a substantial sized, more vulnerable group of patients within this patient category. Some of the questions in the questionnaire may also not be appropriate for the patients in profile 3. Specifically with regards to the questions on the use of technology in daily life, the answers to the question “Do you use technology for work” may be misleading, as the mean age of participants was 69.5 years old and above the average retirement age, compared to the other two groups. We aimed to invite all patients referred to our department during the study period to participate in our study. We do not have the exact number of patients declining to participate in our survey. Due to oversight, we cannot rule out that eligible patients have not been asked to participate. This lack of information on the demographics of these patients is, therefore, a potential weakness of this study. The future impact of these results is difficult to predict. In our population, 20% of patients were allocated to profile 3, which scored the lowest in health technology readiness. These patients were statistically significantly older than patients in the other two groups, and we found that they less often owned a tablet or a smartphone. We have not, however, identified a specific cut-off point regarding age and low scores on the READHY that would enable us to identify patients who may need additional or alternative information. Conclusions Our study found that most patients had medium to high health technology readiness, but we also identified a group with lower health technology readiness. Based on our results, healthcare personnel dealing with women with suspected breast cancer should be aware of patients struggling with health technology. Age and technology familiarity may indicate vulnerable patients. More studies investigating different modes of information delivery with follow-ups are needed. Declarations Funding: This research did not receive any funding. Author contributions: All authors contributed to the study's conception and design. Material preparation, data collection and analysis were performed by MS and MH. The first draft of the manuscript was written by MS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Compliance with Ethical Standards: This study was conducted in accordance with the 1964 Helsinki Declaration. The study was registered at ClinicalTrials.gov before initiation (NCT04745117) and approved by the Danish data protection agency 21/4657. Declaration of conflicts of interest: The authors have no conflicts of interest to declare. References Fortin J, Leblanc M, Elgbeili G, et al (2021) The mental health impacts of receiving a breast cancer diagnosis: A meta-analysis. Br J Cancer 125:1582–1592. https://doi.org/10.1038/s41416-021-01542-3 Early and locally advanced breast cancer: diagnosis and management. National Institute for Health and Care Excellence (NICE), London Kuwabara A, Su S, Krauss J (2020) Utilizing Digital Health Technologies for Patient Education in Lifestyle Medicine. Am J Lifestyle Med 14:137–142. https://doi.org/10.1177/1559827619892547 Schooley B, Singh A, Hikmet N, et al (2020) Integrated Digital Patient Education at the Bedside for Patients with Chronic Conditions: Observational Study. JMIR MHealth UHealth 8:e22947. https://doi.org/10.2196/22947 Dekkers T, Melles M, Groeneveld BS, de Ridder H (2018) Web-Based Patient Education in Orthopedics: Systematic Review. J Med Internet Res 20:e143. https://doi.org/10.2196/jmir.9013 Kayser L, Rossen S, Karnoe A, et al (2019) Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals’ Health Technology Readiness: Initial Testing in a Cancer Rehabilitation Setting. J Med Internet Res 21:e10377. https://doi.org/10.2196/10377 Sollie M Health Technologies Readiness in Breast Cancer Patients. https://clinicaltrials.gov/ct2/show/NCT04745117. Accessed 7 Sep 2022 von Elm E, Altman DG, Egger M, et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet Lond Engl 370:1453–1457. https://doi.org/10.1016/S0140-6736(07)61602-X Kayser L, Karnoe A, Furstrand D, et al (2018) A Multidimensional Tool Based on the eHealth Literacy Framework: Development and Initial Validity Testing of the eHealth Literacy Questionnaire (eHLQ). J Med Internet Res 20:e36. https://doi.org/10.2196/jmir.8371 Osborne RH, Elsworth GR, Whitfield K (2007) The Health Education Impact Questionnaire (heiQ): an outcomes and evaluation measure for patient education and self-management interventions for people with chronic conditions. Patient Educ Couns 66:192–201. https://doi.org/10.1016/j.pec.2006.12.002 Osborne RH, Batterham RW, Elsworth GR, et al (2013) The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health 13:658. https://doi.org/10.1186/1471-2458-13-658 Thorsen IK, Rossen S, Glümer C, et al (2020) Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study. J Med Internet Res 22:e21195. https://doi.org/10.2196/21195 IBM Corp. Released 2021. IBM SPSS Statistics for Macintosh, Version 28.0. Armonk, NY: IBM Corp Gordon NP, Crouch E (2019) Digital Information Technology Use and Patient Preferences for Internet-Based Health Education Modalities: Cross-Sectional Survey Study of Middle-Aged and Older Adults With Chronic Health Conditions. JMIR Aging 2:e12243. https://doi.org/10.2196/12243 Onyeaka HK, Romero P, Healy BC, Celano CM (2021) Age Differences in the Use of Health Information Technology Among Adults in the United States: An Analysis of the Health Information National Trends Survey. J Aging Health 33:147–154. https://doi.org/10.1177/0898264320966266 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2982014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":205046562,"identity":"1e00225b-9ddc-49dc-bca7-81e8f5d87351","order_by":0,"name":"Martin Sollie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYLCCBAYbBgZ25oYDDAzMBsRoYGxIYEgDKmYkRQsDw2GwFgaitOi2n33+4OGO8/b8zIyNBz4wWBsT1GJ2Jt2wIfHM7cSZzYwNB2cwpJsR1nIgjbEhse12gsFhxobDPAyHbQhrOf8MpOWcPVjLH6K03ADbcoBxA0gLMByIcNiNZ4wzEtuSIX7pMUgnwvvn0xg+/myzs+dnbz784UeFtWEDQT2ogKiIHAWjYBSMglFAEAAA49A/xn8MJgsAAAAASUVORK5CYII=","orcid":"","institution":"Odense University Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Sollie","suffix":""},{"id":205046563,"identity":"b40b76b2-88fa-477e-b7f3-7872faecefc1","order_by":1,"name":"Marianne Hansen","email":"","orcid":"","institution":"Odense University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Hansen","suffix":""},{"id":205046564,"identity":"62864431-b308-4e46-adb2-4b697f4848f1","order_by":2,"name":"Jørn Bo Thomsen","email":"","orcid":"","institution":"Odense University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jørn","middleName":"Bo","lastName":"Thomsen","suffix":""}],"badges":[],"createdAt":"2023-05-25 15:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2982014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2982014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37775641,"identity":"b6731917-71a1-4b77-aabc-0f4139af4495","added_by":"auto","created_at":"2023-05-31 14:52:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":949722,"visible":true,"origin":"","legend":"\u003cp\u003eReadiness and Enablement Index for Health Technology (READHY) scale scores for the three identified profiles based on cluster analysis. heiQ: Health Education Impact Questionnaire; HLQ: Health Literacy Questionnaire; eHLQ: eHealth Literacy Questionnaire. Data are presented as mean (SD). heiQ8 was reversed (i.e., a high score indicated a low level of emotional distress)\u003c/p\u003e","description":"","filename":"Figure1SCiC.png","url":"https://assets-eu.researchsquare.com/files/rs-2982014/v1/47b500919162f45dba5d6f88.png"},{"id":37775640,"identity":"53c46fe0-1a83-4223-b864-c8acd00f9f69","added_by":"auto","created_at":"2023-05-31 14:52:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50628,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map illustrating the difference in means between the identified READHY profiles. The colour ranging from green to red tones demonstrates the range of means from low to high on the READHY score.\u003c/p\u003e","description":"","filename":"Figure2SCiC.png","url":"https://assets-eu.researchsquare.com/files/rs-2982014/v1/0de78300f35072edd7cfb23b.png"},{"id":42041871,"identity":"674cc387-d7ea-45c4-81eb-2d2388f3bf2c","added_by":"auto","created_at":"2023-08-23 22:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2982014/v1/d4cae8fd-d77c-4f9a-ba31-eb3fb10688ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBeing diagnosed with breast cancer can negatively impact mental health, with studies showing higher anxiety levels, depressive symptoms, distress and trauma-related symptoms[1]. Following initial diagnosis, the patients often undergo further diagnostics and treatment[2]. These steps are usually initiated and performed as fast as possible to avoid delay in treatment. In close cooperation with the healthcare personnel, the patients make treatment decisions. Patients must receive relevant and comprehensive information to enable them to choose their preferred treatment. \u003c/p\u003e\n\n\u003cp\u003ePreviously this information was mainly given orally in consultations with healthcare personnel or using printed materials. Now, in the technology era, the use of digital solutions for patient information is rapidly increasing, and a growing body of evidence supports the use of digital health technology[3, 4]. These platforms have many advantages, such as being more time- and cost-effective than traditional education interventions. It also enables us to provide relevant information uniformly to all our patients[5]. Though the use of digital information is increasing, we have yet to thoroughly investigate whether patients are ready for this change.\u003c/p\u003e\n\n\u003cp\u003eOne way to evaluate the health technology readiness of our patients is the newly developed Multidimensional Readiness and Enablement Index for Health Technology (READHY) questionnaire[6]. The tool assesses the many aspects of health technology readiness, such as technology skills, emotional distress, social support and ability to engage with digital services. It can also identify subpopulations within patient groups with problems using and understanding health technology[6].\u003c/p\u003e\n\u003cp\u003eThere is a gap in the published literature on health technology readiness among women with newly diagnosed or suspected breast cancer. Therefore, we have designed a prospective study using the READHY tool. \u003c/p\u003e\n\u003cp\u003eThe aims of this study are (1) to identify health technology readiness profiles amongst women with a suspected breast cancer diagnosis. (2) to investigate the differences between these profiles according to sociodemographic factors and their current use of technology. \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe designed this study as a cross-sectional study using the READHY questionnaire.\u003c/p\u003e\n\u003cp\u003eIt was registered at ClinicalTrials.gov before initiation. Identifier: NCT04745117[7]. The study was reported per The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement[8]. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSetting and participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the Department of Plastic Surgery at Odense University Hospital. The participants were constituted by a convenience sample obtained from 01.03.2021 through 31.12.2021. All patients referred to us with suspected breast cancer were asked to participate. Our department receives patients from the area of Funen and the surrounding islands. The exclusion criteria were: not being able to understand and read Danish.\u003c/p\u003e\n\n\u003cp\u003ePatients were referred via the official screening program for breast cancer in Denmark, where all women between 50 - and 69 years are offered a mammogram every two years. If the clinical mammogram raised suspicion of breast cancer, they were referred to us as the closest breast surgery centre. Additionally, patients with clinical suspicion of breast cancer were referred by general practitioners. We asked the patients to complete the questionnaire on their first or second visit to our department.\u003c/p\u003e\n\n\u003cp\u003eThe patients answered the questionnaires electronically using a tablet. If the patients wanted to fill out the questionnaires on paper, this was arranged by the attending nurse. The patients were informed orally and in writing about the study before giving consent to participate in the study. The questionnaire constituted a section of demographic data and the READHY tool. \u003c/p\u003e\n\n\u003cp\u003eWe collected the following demographic data: gender, age, the highest level of education, cohabitation status, and source of income. We also asked the participants if they owned a smartwatch, tablet, or computer and their main purpose when using information technology (IT). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Readiness and Enablement Index for Health Technology (READHY)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe READHY tool was used to assess readiness for health technology. The tool consists of the eHealth Literacy Questionnaire (eHLQ)[9], which includes seven scales, supplemented with four scales from the Health Education Impact Questionnaire (heiQ)[10] and two scales from the Health Literacy Questionnaire (HLQ)[11]. Together, these scales capture eHealth literacy, self-management, and social context. The READHY tool is a validated assessment tool[6]. The 13 scales were assessed using 65 items. Each item was presented to the participant as a statement and scored on a 4-point rating scale, ranging from 1=strongly disagree to 4=strongly agree. The overall score of each scale was calculated as the mean score of the 4\u0026ndash;6 items (i.e., statements) that constitute the scale. If less than 50% of items in a scale were answered, the scale was regarded as missing; in those cases, the survey was considered incomplete.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical methods\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eWe consulted a clinical statistician before initiating the study. Based on previously published data using the READHY tool, we were advised to aim for approximately one hundred participants. \u003c/p\u003e\n\n\u003cp\u003eOur data-analysis method was based on a previously published study with a similar aim by Thorsen et al. [12]. We used a data-driven approach with a combination of hierarchical and K-means cluster analysis to divide participants into clusters depending on their level of readiness for health technology. We have chosen to refer to these clusters as profiles. \u003c/p\u003e\n\u003cp\u003eWe used the two-step k-means analysis and Bonferroni post hoc analyses using the one-way analysis of variance (ANOVA) to decide the optimal number of clusters. The results assessed three profiles as the best fit and two as the second best fit. We conducted K-means cluster analyses for three profiles in 8 iterations. The difference between profiles for each ready scale was assessed using a one-way ANOVA.\u003c/p\u003e\n\n\u003cp\u003eFor sociodemographic and IT-related data, the differences between the profiles were tested using the Fisher exact test for frequencies and the one-way ANOVA for continuous variables. Frequencies are reported as numbers and proportions, and continuous variables are reported as mean and standard deviation (SD). \u003c/p\u003e\n\n\u003cp\u003eData were analysed as observed. No imputations were used to replace missing data. \u003c/p\u003e\n\u003cp\u003eData were analysed using SPSS version 28[13]. The statistical analyses were performed under the guidance of a clinical statistician. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReadiness for Health Technology\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cluster analyses of the total READHY scores resulted in three distinct profiles of participants. They are presented in Figures 1 and 2. The different profiles scored high, medium and low in their overall READHY score. Profile 2 (n=18) consistently scored high on all scales, while profile 3 (20) revealed low scores. Profile 1 (n=54) consistently scored medium on all scales. 60% of participants were allocated to profile 1. The other two profiles, 2 and 3, comprised 20% of the participants each.\u003c/p\u003e\n\u003cp\u003eProfile 3 scored lowest on all parameters when assessing the READHY items regarding health literacy, the eHLQ and HLQ, indicating that these patients felt less supported by their healthcare providers and social network. They also reported low motivation to engage with digital services and insufficient access to digital services that suit their needs.\u003c/p\u003e\n\u003cp\u003eThe same pattern was seen in the heiQ, where profile 3 also consistently scores the lowest, indicating problems with self-monitoring and insight, constructive attitudes and approaches to health education. They also reported a higher emotional stress level than the other two profiles.\u003c/p\u003e\n\u003cp\u003e[Figure 1]\u003c/p\u003e\n\u003cp\u003e[Figure 2]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSociodemographic Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sociodemographic characteristics of the total population and the individual profiles are presented in Table 1. When comparing the different profiles, we observed that participants in profile 2, with the highest scores, were younger (57.9 y) than the participants in profiles 1 (62.5 y) and 3 (69.5 y) (P=0.013). We did not detect any statistically significant differences in the groups regarding their highest level of education, cohabitation status or source of income. Profile 3 had the highest portion of patients, with only comprehensive school as their highest level of education. Yet, the frequency differences were not statistically significantly different from the other two profiles. Regarding cohabitation status, there was a tendency for patients in profile 3 more often lived alone compared to the other two groups, but no statistically significant difference was detected. Patients in profile 3 had the lowest frequency of salary as income, with a higher portion receiving retirement pension or public income support.\u003c/p\u003e\n\u003cp\u003e[Table 1]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Sociodemographic characteristics and access to technology of participants (N=92) across the identified profiles. Data are presented as mean (SD) for continuous variables and number (proportions) for frequencies.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacterstics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 1\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 2\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 3\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e63.1 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e62.5 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e57.9 (12.7) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e69.5 (11.1) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest attained level of education, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.277\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eComprehensive school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e16 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eShort education (2-3 y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e40 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e25 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedium education (3-4 y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e34 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e20 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eLong education (4+ y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohabitation status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.451\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e23 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e13 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eLiving with spouse and/or children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e69 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e41 (75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e13 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.650\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e38 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e25 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e10 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e3 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eRetirement pension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e11 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e4 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003ePublic income support/ no incomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e43 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e23 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e13 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you own any of these IT aids?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eSmartwatch?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.845\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e13 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e79 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e46 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e18 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eSmartphone?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e85 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e53 (98.1) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (75.0) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (1.9) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5 (25.0) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eComputer?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.130\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e77 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e46 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e14 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eTablet?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.030\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e64 (69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e40 (74.1) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e16 (88.8) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (40.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e28 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e14 (25.9) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2 (11.1) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e12 (60.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow do you use technology in your daily life?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eExcersize?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (14.8) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (44.4) a,c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.0) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e75 (8,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e46 (85.2) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e10 (55.5) a,c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e19 (95.0) b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eWork?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e38 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e23 (42.6) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e12 (66.7) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e3 (15.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e54 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e31 (57.4) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6 (33.3) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (85.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eSeeking information?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e76 (82,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e47 (92.2) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (94.4) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e12 (60.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e16 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7 (13.7) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (40.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eCommunication?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e0.496\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e77 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e46 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e16 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eEntertainment?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e62 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e39 (72.2) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e17 (94.4) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6 (30.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.78947368421053%\" valign=\"bottom\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e30 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e15 (27.8) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1 (5.6) c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e14 (70.0) a,b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.9090909090909%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ea) Different than profile 1\u0026nbsp;\u003cbr\u003e\u0026nbsp;b) Different than profile 2\u0026nbsp;\u003cbr\u003e\u0026nbsp;c) Different than profile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIT use\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reported IT use is presented in Table 1. The participants in the lowest scoring, profile 3, had a statistically significant lower percentage of smartphone ownership (75%) compared to profiles 1 (98%) and 2 (94%) (p=0.006). The same was observed regarding tablet ownership, with a rate of 40% in profile 3 and 74% and 89% in profiles 1 and 2, respectively (p=0.03). We did not detect any significant difference in computer and smartwatch ownership frequencies.\u003c/p\u003e\n\u003cp\u003eWhen asked about their use of IT in daily life, the youngest group, profile 2, had the highest use of technology during fitness and exercise (15%), compared to the two other profiles, 1 (15%) and 3 (5%). The difference was statistically significant across the profiles (p=0.006). Similar results were observed for the variable \u0026ldquo;IT in work situations\u0026rdquo;. When asked about their use of IT in seeking information, almost all participants in profiles 1 and 2 stated yes, while the frequency was much lower in profile 3 (92.2%, 94,4% and 60.0%, respectively. p=0.009). For communication purposes, there was no statistically significant difference across the groups. Profile 2 used the most IT for entertainment purposes (94.4%), while profile 1 (72.2%) and profile 3 (30.0%) stated much lower usage (P=\u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study investigating the health technology readiness amongst patients referred to the hospital with suspected breast cancer using the READHY tool.\u003c/p\u003e \u003cp\u003eMost patients, around 80%, demonstrated medium to high levels of health technology readiness. Our study's main finding is identifying patients with lower READHY scores in all the measured parameters. This group comprised around 20% of the total participants in our sample. This group had the lowest scores on the Health Education Impact Questionnaire (heiQ), indicating a higher level of emotional stress, less self-monitoring and health insight, and a lower skill set to manage their own health. The same pattern was seen in the eHealth Literacy Questionnaire (HLQ), with more issues related to locating, using, and understanding digital healthcare information. They also scored the lowest on The Health Literacy Questionnaire (HLQ) when asked about the support system surrounding their health, their ability to take action regarding their healthcare, and their understanding of information about their health. This is in line with other studies identifying subpopulations of lower health technology readiness in other patient groups, such as diabetes type II. Factors associated with these subpopulations were, amongst others, age, emotional well-being, familiarity with IT and degree of eHealth literacy[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe investigated sociodemographic factors, and IT use to find some factors that could help us locate these potentially more vulnerable patients. The only sociodemographic factor related to profile 3 was age, as these patients were statistically significantly older than the other two. We also observed that patients in profile 3 were less likely to be familiar with the use of technology regarding exercise, seeking information and entertainment purposes. This is not surprising as previous studies have found that older patients may not be able to or wish to engage with electronic health resources[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main limitation of this study is the cross-sectional design, with a lack of follow-up. We have no data on how the patients eventually received and understood the information given to them. We did not register the diagnostics journey of our patient before being referred to our department, and there is a risk that being referred from the screening program or via t the general practitioner could influence the READHY results, especially regarding the items of emotional distress. Our convenience sample constituted only ninety-two patients, which could be low when making a cluster analysis. We do, however, feel that the results clearly indicate a substantial sized, more vulnerable group of patients within this patient category.\u003c/p\u003e \u003cp\u003eSome of the questions in the questionnaire may also not be appropriate for the patients in profile 3. Specifically with regards to the questions on the use of technology in daily life, the answers to the question \u0026ldquo;Do you use technology for work\u0026rdquo; may be misleading, as the mean age of participants was 69.5 years old and above the average retirement age, compared to the other two groups.\u003c/p\u003e \u003cp\u003e We aimed to invite all patients referred to our department during the study period to participate in our study. We do not have the exact number of patients declining to participate in our survey. Due to oversight, we cannot rule out that eligible patients have not been asked to participate. This lack of information on the demographics of these patients is, therefore, a potential weakness of this study.\u003c/p\u003e \u003cp\u003eThe future impact of these results is difficult to predict. In our population, 20% of patients were allocated to profile 3, which scored the lowest in health technology readiness. These patients were statistically significantly older than patients in the other two groups, and we found that they less often owned a tablet or a smartphone. We have not, however, identified a specific cut-off point regarding age and low scores on the READHY that would enable us to identify patients who may need additional or alternative information.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study found that most patients had medium to high health technology readiness, but we also identified a group with lower health technology readiness. Based on our results, healthcare personnel dealing with women with suspected breast cancer should be aware of patients struggling with health technology. Age and technology familiarity may indicate vulnerable patients. More studies investigating different modes of information delivery with follow-ups are needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026apos;s conception and design. Material preparation, data collection and analysis were performed by MS and MH. The first draft of the manuscript was written by MS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the 1964 Helsinki Declaration. The study was registered at ClinicalTrials.gov before initiation (NCT04745117) and approved by the Danish data protection agency 21/4657. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicts of interest:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFortin J, Leblanc M, Elgbeili G, et al (2021) The mental health impacts of receiving a breast cancer diagnosis: A meta-analysis. Br J Cancer 125:1582\u0026ndash;1592. https://doi.org/10.1038/s41416-021-01542-3\u003c/li\u003e\n\u003cli\u003eEarly and locally advanced breast cancer: diagnosis and management. National Institute for Health and Care Excellence (NICE), London\u003c/li\u003e\n\u003cli\u003eKuwabara A, Su S, Krauss J (2020) Utilizing Digital Health Technologies for Patient Education in Lifestyle Medicine. Am J Lifestyle Med 14:137\u0026ndash;142. https://doi.org/10.1177/1559827619892547\u003c/li\u003e\n\u003cli\u003eSchooley B, Singh A, Hikmet N, et al (2020) Integrated Digital Patient Education at the Bedside for Patients with Chronic Conditions: Observational Study. JMIR MHealth UHealth 8:e22947. https://doi.org/10.2196/22947\u003c/li\u003e\n\u003cli\u003eDekkers T, Melles M, Groeneveld BS, de Ridder H (2018) Web-Based Patient Education in Orthopedics: Systematic Review. J Med Internet Res 20:e143. https://doi.org/10.2196/jmir.9013\u003c/li\u003e\n\u003cli\u003eKayser L, Rossen S, Karnoe A, et al (2019) Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals\u0026rsquo; Health Technology Readiness: Initial Testing in a Cancer Rehabilitation Setting. J Med Internet Res 21:e10377. https://doi.org/10.2196/10377\u003c/li\u003e\n\u003cli\u003eSollie M Health Technologies Readiness in Breast Cancer Patients. https://clinicaltrials.gov/ct2/show/NCT04745117. Accessed 7 Sep 2022\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet Lond Engl 370:1453\u0026ndash;1457. https://doi.org/10.1016/S0140-6736(07)61602-X\u003c/li\u003e\n\u003cli\u003eKayser L, Karnoe A, Furstrand D, et al (2018) A Multidimensional Tool Based on the eHealth Literacy Framework: Development and Initial Validity Testing of the eHealth Literacy Questionnaire (eHLQ). J Med Internet Res 20:e36. https://doi.org/10.2196/jmir.8371\u003c/li\u003e\n\u003cli\u003eOsborne RH, Elsworth GR, Whitfield K (2007) The Health Education Impact Questionnaire (heiQ): an outcomes and evaluation measure for patient education and self-management interventions for people with chronic conditions. Patient Educ Couns 66:192\u0026ndash;201. https://doi.org/10.1016/j.pec.2006.12.002\u003c/li\u003e\n\u003cli\u003eOsborne RH, Batterham RW, Elsworth GR, et al (2013) The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health 13:658. https://doi.org/10.1186/1471-2458-13-658\u003c/li\u003e\n\u003cli\u003eThorsen IK, Rossen S, Gl\u0026uuml;mer C, et al (2020) Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study. J Med Internet Res 22:e21195. https://doi.org/10.2196/21195\u003c/li\u003e\n\u003cli\u003eIBM Corp. Released 2021. IBM SPSS Statistics for Macintosh, Version 28.0. Armonk, NY: IBM Corp\u003c/li\u003e\n\u003cli\u003eGordon NP, Crouch E (2019) Digital Information Technology Use and Patient Preferences for Internet-Based Health Education Modalities: Cross-Sectional Survey Study of Middle-Aged and Older Adults With Chronic Health Conditions. JMIR Aging 2:e12243. https://doi.org/10.2196/12243\u003c/li\u003e\n\u003cli\u003eOnyeaka HK, Romero P, Healy BC, Celano CM (2021) Age Differences in the Use of Health Information Technology Among Adults in the United States: An Analysis of the Health Information National Trends Survey. J Aging Health 33:147\u0026ndash;154. https://doi.org/10.1177/0898264320966266\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Health Technology Readiness, Health Technology","lastPublishedDoi":"10.21203/rs.3.rs-2982014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2982014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation technologies are increasingly used when informing patients about their disease, treatment and prognosis. These digital platforms have many advantages compared to traditional education interventions. There are concerns that some patients may have difficulty with this mode of information delivery. This warrants the question; are our patients ready for the transition towards more digital information technologies?\u003c/p\u003e\n\u003cp\u003eWe aimed to assess health technology readiness profiles amongst women with a suspected breast cancer diagnosis. Secondly, we wanted to investigate the potential differences between these profiles according to sociodemographic factors and the patients´ current use of technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study used the Readiness and Enablement Index for Health Technology (READHY) questionnaire. We included all patients (n=92) referred with suspected breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cluster analyses identified three distinct profiles. Patients in profile 1 (n=54) demonstrated medium health technology readiness. Profile 2 (n=18) reported high scores on all parameters. Profile 3 (n=20) scored lowest on all parameters indicating problems with health literacy, eHealth literacy and insight into their health. Profile 3 also reported higher levels of emotional stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study found that most patients had medium to high health technology readiness, but we also identified a group with lower health technology readiness. Based on our results, healthcare personnel dealing with women with suspected breast cancer should be aware of patients struggling with health technology. Age and technology familiarity may indicate vulnerable patients. More studies investigating different modes of information delivery with follow-ups are needed.\u003c/p\u003e","manuscriptTitle":"Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-31 14:52:15","doi":"10.21203/rs.3.rs-2982014/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0abed6e4-213f-409f-b98f-8d8834b99d02","owner":[],"postedDate":"May 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-23T21:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-31 14:52:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2982014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2982014","identity":"rs-2982014","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}