Digital readiness among 3,555 individuals with hip or knee osteoarthritis initiating a supervised education and exercise therapy program: a cross-sectional study

Digital readiness among 3,555 individuals with hip or knee osteoarthritis initiating a supervised education and exercise therapy program: 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 Digital readiness among 3,555 individuals with hip or knee osteoarthritis initiating a supervised education and exercise therapy program: a cross-sectional study Graziella Zangger, Dorte T. Grønne, Lars H. Tang, Lau C. Thygesen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6312226/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jun, 2025 Read the published version in Musculoskeletal Care → Version 1 posted You are reading this latest preprint version Abstract Background Digital health solutions can support exercise and symptom management in hip and knee osteoarthritis (OA), but their uptake may depend on digital readiness, as measurement of motivation, confidence, and capability in using digital solutions. This study assesses digital readiness profiles in individuals with hip and knee OA starting in-person physiotherapist-supervised exercise therapy and education in primary care (GLA:D®) and their associations with sociodemographic, health, and functional characteristics. Methods Baseline GLA:D® registry questionnaire data were analyzed. The eHealth Readiness Scale (7–42, lowest to highest) measures digital readiness (e.g., capability to use digital health solutions). Latent class analysis identified digital readiness profiles, and multinomial logistic regression was used to assess associations with selected characteristics and the identified profiles. Results We included 3,555 participants (mean age 66.7 years, 67% female), with a mean digital readiness score of 25.6 (SD 8.1). Confidence in internet use was reported by 53%, whereas 32% agreed that it improved efficiency, but only 26% agreed to use lifestyle tracking devices. Three digital readiness profiles emerged: low, intermediate, and high. Compared with the high profile, the low profile was associated with older age (odds ratio (OR) 1.96, 95% confidence interval (CI) 1.71 to 2.24)), being female (OR 0.72, 95% CI 0.57 to 0.90), having a lower education level (OR 0.62, 95% CI 0.45 to 0.88), living alone (OR 1.39, 95% CI 1.11 to 1.76), and having more comorbidities (OR 1.10, 95% CI 1.04 to 1.17). The intermediate profile showed similar patterns in relation to the high profile but participants in the intermediate profile were also less likely among obese individuals (0.70, 95% CI 0.54 to 0.91) and those with higher walking speeds (0.70, 95% CI 0.50 to 0.98). Conclusion The three identified digital readiness profiles and associated characteristics such as age, sex, education, and comorbidities emphasize the potential of assessing digital readiness to improve uptake and resource allocation when designing and implementing digital health solutions in clinical settings. Future research should focus on digital readiness improvement strategies. Digital readiness Digital health Laten class analysis Osteoarthritis Exercise therapy Health education Figures Figure 1 Figure 2 Background The high and increasing prevalence of hip and knee osteoarthritis (OA) [ 1 ] highlights the importance of innovative approaches to disease management, including the integration of digital health solutions. Telehealth platforms, smartphone applications (apps), and wearable devices can provide continuous personalized support and care, monitor symptoms, and facilitate remote communication, which is essential for managing chronic conditions such as hip and knee OA [ 2 ]. Digital solutions further offer benefits such as reduced travel, shorter waiting times, lower costs, greater flexibility, and intensified and practical care [ 3 ]. Adapting to digital health solutions is becoming increasingly important in healthcare [ 4 ], and embracing digital health solutions in hip or knee OA management represents a proactive approach to mitigating the long-term impacts of OA on both the individual and healthcare systems [ 5 – 7 ]. Digitally delivered exercise programs are particularly intriguing, as they offer an alternative to traditional rehabilitation for individuals with hip and knee OA [ 8 , 9 ], especially for those with severe functional limitations or commitments that hinder in-person participation [ 3 , 9 , 10 ]. However, common barriers to adoption include privacy concerns, reduced personal interaction, and doubts about the credibility of digital solutions [ 3 ]. In addition, a persistent digital divide affects older adults, individuals with low literacy, and those from socioeconomically disadvantaged backgrounds, who may face challenges in accessing and using digital technologies effectively [ 3 ]. These disparities highlight that not all are equally ready to engage with digital health solutions. While existing studies have demonstrated several health benefits of digitally delivered exercise for individuals with hip or knee OA [ 11 – 15 ], a knowledge gap remains in understanding how ready individuals with hip or knee OA are to start engaging with these digital health solutions [ 16 ]. This can be determined by measuring digital readiness, which refers to the combination of motivation, willingness, confidence, and capability needed to initially accept and engage with digital health solutions [ 17 , 18 ]. Various frameworks have explored readiness to use digital health, suggesting that diverse personal, social, and contextual factors influence digital readiness [ 19 – 22 ]. While closely related to digital health literacy—the ability to find, understand, and use health information via technology [ 23 ]—digital readiness may extend beyond this by incorporating emotional and social dimensions of use [ 20 ]. Using the eHealth Readiness Scale level to assess digital readiness, self-efficacy (a person's confidence in the ability to use digital solutions) is emphasized as a central component. The eHealth Readiness scale provides a low-burden measurement tool that accounts for both the psychological and practical aspects of engaging with digital health solutions. Understanding digital readiness is essential for optimizing the implementation and uptake of digital health interventions, as users must go beyond the first access to meaningfully engage with these solutions to succeed. Furthermore, identifying and stratifying individuals based on digital readiness and related factors may enable more tailored and inclusive intervention strategies, ensuring that digital health interventions are accessible, effective, and aligned with user needs. Digital readiness can, therefore, be a pivotal aspect of maximizing potential benefits and serves as a foundation for the successful design, implementation, and sustainability of digital health solutions [ 24 – 27 ]. This study, therefore, aims to investigate digital readiness profiles among people with hip or knee OA initiating in-person physiotherapist-supervised exercise therapy and education in primary care (GLA:D)® and assess associations with sociodemographic and health characteristics and performance-based functional test outcomes with the established digital readiness profiles. Methods The study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [ 28 ]. The STROBE checklist can be found in Supplementary Table 1. The statistical analysis plan was preregistered before analysis and is available at www.osf.io/sbpw4 . Study design and data sources This cross-sectional study uses national GLA:D® registry data. GLA:D® offers guideline-aligned group education and supervised exercise therapy for people with hip or knee OA [ 29 ] through 2–3 90-minute (min) education sessions and 12 60-min supervised neuromuscular exercise sessions delivered twice weekly for six weeks. Approximately 6,000 participants initiate GLA:D® annually through self-referral or from a general practitioner at one of the nearly 300 Danish physiotherapy clinics, either private (with ~ 40% public reimbursement) or public (free attendance). The GLA:D® registry collects data from therapists and participants at baseline, three months, and 12 months after baseline; only baseline data are used in this study. Participants received an emailed link to an online questionnaire with up to two reminders after one week, and onsite kiosk options were available for participants without email. Participants Participants who initiated GLA:D® between March 7, 2022, and January 5, 2023, were included, as digital readiness information was collected during this period. The inclusion criterion in GLA:D® is a clinical hip or knee OA diagnosis. The exclusion criteria are reasons other than OA for joint problems (e.g., tumor, inflammatory joint disease, or sequelae after a hip fracture), having other competing and more severe symptoms than OA problems (e.g., chronic, generalized pain, or fibromyalgia), and being unable to read and understand Danish. Radiographic images are not needed for a clinical diagnosis of OA[ 30 ] and, therefore, are not a criterion for entering the GLA:D®. Variables of interest Digital readiness The eHealth Readiness Scale was developed in 2016 by Bhalla et al . to measure participants' readiness to engage in eHealth or digital health interventions (digital readiness) [ 31 ]. The scale items are based on Bandura's theory on self-efficacy, previous literature, and measurement scales [ 31 ]. The 7-item scale is scored on a 6-point Likert-type scale (1 = strongly disagree and 6 = strongly agree), and scores range from 7 to 42, with higher scores indicating greater readiness with no presiding thresholds of (in)sufficient readiness [ 31 ]. The scale has previously demonstrated good psychometric properties, with a robust unidimensional scale and high internal consistency (Cronbach's alpha 0.81) [ 31 ]. While Bhalla et al . assessed digital readiness in the continuation of a digital health intervention [ 31 ], we assessed baseline readiness without referencing a specific digital solution or measuring uptake in this current study. Linguistic and cross-cultural validation The eHealth Readiness Scale, including the introduction text, was independently forward and backward translated from English to Danish by two health professionals (one native Danish speaker fluent in English and one native English speaker fluent in Danish). Dr. Bhalla approved the use and translation of the scale. Two patient-partners reviewed and commented on the Danish translation. A third researcher resolved discrepancies, incorporating patient feedback to finalize the Danish version (Supplementary Table 2). Digital readiness profiles As the eHealth Readiness Scale has no approved cutoff scores for readiness levels, an explorative approach was chosen using latent class analysis to identify subgroups on the basis of the participant responses to the scale items. Sociodemographic characteristics Age and sex were derived from the Danish Civil Registration (CPR) System [ 32 ], with age continuously calculated from the initial visit date. Sex was recorded as male/female. We recorded whether participants were born in Denmark and had Danish citizenship as binary variables (yes/no). Birthplace and citizenship were included as proxies for language proficiency and cultural integration. Education level was based on the highest level completed and categorized into no, primary, or lower secondary (collapsed), upper secondary, and higher education. Cohabitation status was recorded, indicating whether the participant lived alone or with others. Health characteristics Body mass index (BMI) was calculated on the basis of body weight in kilograms and height in centimeters (measured by the therapist), which was subsequently categorized into < 18.4 to 24.9 for underweight and normal weight, 25.0 to 29.9 for preobese, and ≥ 30 for obese classes I to III [ 33 ]. The participants indicated the most affected (symptomatic) hip or knee joint. Therapists recorded the duration of symptoms in months for the most affected joint. The pain intensity (of the most affected joint) during the last week was measured via the visual analog scale (VAS) [ 34 ], which ranges from 0-100 mm (indicating 'no pain' to 'maximum pain'). Therapists recorded analgesic use, including acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioid medication, over the preceding two weeks. Comorbidities were assessed by quantifying the number of self-reported health conditions from 30 possibilities. Moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) were based on self-reported time spent engaging in either intensity on a typical week (MVPA: 300 min; VPA: 150 min). Physical activity compliance was determined in a binary manner, adhering to the WHO minimum recommendations for adult physical activity (≥ 150 min of moderate physical activity (MPA), ≥ 75 min of VPA, or an equivalent combination). This calculation was similar to the Nordic Physical Activity Questionnaire-short validation method [ 35 ]. Sedentary behavior was calculated by typically daily sitting time during transportation, work/school, leisure/screen time, or other, categorized with a threshold of ≥ 9 hours per day indicating sedentary behavior [ 36 ]. Values > 16 hours for work/school, > 6 hours for other sedentary behaviors, or > 24 hours were excluded. The summary scores of the Hip disability and Osteoarthritis Outcome Score 12 (HOOS-12) and the Knee disability and Osteoarthritis Outcome Score 12 (KOOS-12) questionnaires were utilized to assess pain, function, and quality of life. Scores are given on a scale of 0-100 (higher scores indicate better quality of life) [ 37 , 38 ]. The EuroQol 5-Dimensions (EQ-5D) 5-Level assesses health-related quality of life across five dimensions, with five response levels in an index score. The index scores were based on the Danish value set [ 39 ]. Furthermore, the EQ-5D VAS scores overall self-rated health from 0-100 (higher scores indicate better quality of life) [ 39 ]. Functional performance-based tests Functional performance was assessed using the 30-second chair-stand test and the 40-meter fast-paced walk test, as recommended by the Osteoarthritis Research Society International (OARSI) [ 40 ]. Both tests were administered once at the respective clinics according to OARSI protocols [ 40 ]. The chair-stand test recorded the number of completed stands in 30 seconds, while the walk test measured walking speed (m/s) over 40 meters. The use of walking aids during testing was documented. Nonresponders A nonresponder analysis was performed for the available items: age, sex, BMI, most affected joint, analgesic use, results from the 30-second chair-stand test and the 40-meter fast-paced walk test, as well as available email contact. Statistical analysis Descriptive statistics were computed to summarize participants' sociodemographic and health characteristics, performance-based test results, and responses to the eHealth Readiness Scale items. Categorical data are presented as absolute frequencies and percentages, whereas continuous data are expressed as the means with standard deviations (SDs) or medians (IQRs) as appropriate. Scale reliability was tested via Cronbach's alpha, interitem, and item-rest tests for internal consistency. Latent class analysis was performed via Mplus 8.10[ 41 ]. Latent class analysis is a probabilistic model-based technique used to categorize a sample into distinct and exhaustive subgroups according to their response pattern to the eHealth Readiness Scale [ 42 ]. Models with one to five classes were assessed, including models where the response categories were collapsed for similar answer categories (i.e., strongly agree with agree, mildly agree with mildly disagree, and strongly disagree with disagree). The final number of classes was determined on the basis of conceptual meaning, subgroup size, and entropy statistics, indicating the certainty of subgroup membership [ 43 ]. Maximum likelihood estimation via the expectation-maximization procedure was employed with automatically generated random starting values and 1,000 iterations to enhance generalizability. Entropy statistics were evaluated at a threshold of ≥ 0.80 to ascertain the optimal number of latent classes. Multinomial logistic regressions followed class determination and examined associations between digital readiness profiles (categorical outcome) and selected sociodemographic, health, and performance-based variables (exposures) in a single model. One readiness profile was set as the base reference. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Age was rescaled by 10 to reflect a decade-level increase; VAS pain, KOOS-12/HOOS-12, and EQ-5D VAS scores were similarly rescaled to reflect a 10-point decrease. Only the EQ-5D VAS was included in the multinomial logistic regressions to avoid multicollinearity. Multicollinearity was assessed via variance inflation factors (VIF) from linear regression using the same predictors. All VIFs were below 3.1; see Supplementary Table 3. Potential confounders were assessed by adding age, sex, BMI, education, and sedentary behavior stepwise to unadjusted multinomial logistic models to examine changes in odds ratios. Interaction effects between age, sex, BMI, and education were tested using models with interaction terms to assess whether associations with digital readiness profiles varied by combined characteristics. The data were analyzed via STATA version 18 [ 44 ] at a significance level of p < 0.05 (two-tailed). Results Participant characteristics A total of 3,555 out of 4,776 participants responded to the survey (74%) and were included in the study. The participants were primarily older adults (mean age 66.4 years, SD 9.6), with a majority being female (67%) and the knee being the most affected joint (Table 1 ). Table 1 Sociodemographic and health characteristics and performance-based functional test of the total group and the three digital readiness profiles Variable All Low Digital Readiness Profile Intermediate Digital Readiness Profile High Digital Readiness Profile Participants, n (%) 3555 740 (20.8%) 1528 (43.0%) 1287 (36.2%) Age at first visit, mean (SD) 66.4 (9.6) 70.4 (8.9) 66.7 (9.4) 63.8 (9.4) Female, n (%) 2386 (67.1%) 514 (69.5%) 1083 (70.9%) 789 (61.3%) Born in Denmark, n (%) 3414 (96.0%) 716 (96.8%) 1470 (96.2%) 1228 (95.4%) Danish citizenship, n (%) 3500 (98.5%) 733 (99.1%) 1509 (98.8%) 1258 (97.7%) Education level, n (%) No, primary, or lower secondary education 376 (10.6%) 128 (17.3%) 164 (10.7%) 84 (6.5%) Upper secondary education 1675 (47.1%) 360 (48.6%) 761 (49.8%) 554 (43.0%) Higher education 1504 (42.3%) 252 (34.1%) 603 (39.5%) 649 (50.4%) Cohabitate status, living alone, n (%) 945 (26.6%) 248 (33.5%) 417 (27.3%) 280 (21.8%) BMI, mean (SD) 28.7 (5.5) 28.3 (5.4) 28.6 (5.6) 29.0 (5.3) BMI category, n (%)* Underweight/normal weight 942 (26.5%) 218 (29.5%) 420 (27.5%) 304 (23.6%) Preobese 1351 (38.0%) 278 (37.6%) 581 (38.0%) 492 (38.2%) Obese (class I, II, or III) 1215 (34.2%) 238 (32.2%) 509 (33.3%) 468 (36.4%) Self-reported most affected joint, n (%) Knee 2362 (66.4%) 485 (65.5%) 1022 (66.9%) 855 (66.4%) Hip 1193 (33.6%) 255 (34.5%) 506 (33.1%) 432 (33.6%) Bilateral symptoms, n (%) 1350 (38.0%) 263 (35.5%) 555 (36.3%) 532 (41.3%) Symptoms length of most affected joint in months, median (IQR) 12.0 (6.0–27.0) 12.0 (6.0–30.0) 12.0 (5.0–24.0) 12.0 (5.0–30.0) Hip or knee pain during the last week (0-100), mean (SD) 47.0 (22.8) 48.3 (23.8) 47.1 (22.3) 46.0 (22.8) Taking pain medications, n (%) 2277 (64.1%) 487 (65.8%) 988 (64.7%) 802 (62.3%) Number of comorbidities, mean (SD) 2.4 (1.9) 2.9 (2.0) 2.4 (1.9) 2.2 (1.8) Three most common comorbidities, n (%)** Hypertension 1525 (42.9%) 382 (51.6%) 637 (41.7%) 506 (39.3%) Back pain 1092 (30.8%) 259 (35.0%) 470 (30.8%) 363 (28.2%) Hypercholesterolemia 1083 (30.5%) 268 (36.2%) 466 (30.5%) 349 (27.1%) Moderate to vigorous physical activity, n (%) < 30 min. 783 (22.0%) 335 (21.9%) 187 (25.3%) 261 (20.3%) 30–89 min. 1024 (28.8%) 445 (29.1%) 219 (29.6%) 360 (28.0%) 90–149 min. 688 (19.4%) 302 (19.8%) 112 (15.1%) 274 (21.3%) 150–299 min. 724 (20.4%) 318 (20.8%) 142 (19.2%) 264 (20.5%) > 300 min. 336 (9.5%) 128 (8.4%) 80 (10.8%) 128 (9.9%) Noncompliant with WHO's minimum recommendations for physical activity, n (%) 2389 (67.2%) 496 (67.0%) 1044 (68.3%) 849 (66.0%) Sedentary behavior (sitting > 9 h/day), n (%)*** 1633 (45.9%) 235 (31.9%) 607 (39.8%) 595 (46.4%) eHealth Readiness Scale score, range 7–42, mean (SD) 25.9 (8.1) 14.0 (4.1) 25.3 (3.4) 33.7 (3.9) KOOS/HOOS 12 summary score, mean (SD) 51.5 (14.6) 51.0 (15.7) 51.0 (14.4) 52.1 (14.9) EQ-5D index, mean (SD) 0.758 (0.202) 0.743 (0.218) 0.754 (0.202) 0.772 (0.192) EQ-5D VAS, mean (SD) 67.4 (19.2) 65.1 (20.9) 67.3 (18.8) 68.7 (18.7) Number of chair stands (30 seconds chair stand test), mean (SD) 11.9 (4.2) 11.1 (3.9) 11.8 (4.1) 12.5 (4.3) Walking speed in meters per second (40 meters walk test), mean (SD) 1.47 (0.35) 1.38 (0.36) 1.45 (0.33) 1.55 (0.35) BMI = Body Mass Index; EQ-5D = EuroQol 5-Dimension 5-Level; HOOS = Hip disability and Osteoarthritis Outcome Score; IQR = Interquartile range; KOOS = Knee injury and Osteoarthritis Outcome Score; SD = Standard deviation; VAS = Visual Analog Scale; WHO = World Health Organization. *n = 47 participants had missing BMI data **Differences in prevalence of the three most common comorbidities across digital readiness profiles assessed by Chi-square test (χ²) and found significant (p ≤ 0.007) ***n = 11 participants were excluded from the analysis due to missing data or sedentary time limits Nonresponders The 1,221 nonresponders were older (mean age 67.3 years, SD 11.0) and more often lacked an email contact (8.7%). The nonresponders also had the knee as the most symptomatic joint but performed worse on the functional performance-based tests than the responders (Supplementary Table 4). Digital readiness Reliability of the eHealth Readiness Scale The Cronbach's alpha of the eHealth Readiness Scale was 0.90, with high item‒test scores (range 0.72–0.85) and average interitem covariance (1.20) (Supplementary Tables 5 and 6). eHealth Readiness responses The mean eHealth Readiness Scale score was 25.9 (SD 8.1). More than half of the participants (53.0%) agreed or strongly agreed that they could make good use of the internet, web apps, or apps (item 4), whereas 21.4% disagreed or strongly disagreed. However, only 32.4% agreed or strongly agreed that internet technologies made them more efficient (item 3), and 26.3% of the participants agreed or strongly agreed to use an internet-connected device to track their lifestyle (item 7), whereas 46.8% disagreed or strongly disagreed (Fig. 1 ). Digital readiness profiles Five latent class analysis models were assessed. An even participant distribution within the 3-, 4-, and 5-class models (Supplementary Tables 7 to 10) was found, and all had a good statistical fit (entropy ≥ 0.87). Consequently, we opted for a simplified model comprising the three classes with collapsed response categories, as this model aligned well with the interpretability and clinical meaningfulness of the eHealth Readiness Scale. The 3-class model showed more distinct digital readiness profiles than the 4- and 5-class models did, and the mean probabilities for class membership were high (0.94 for class 1 (low digital readiness), 0.94 for class 2 (intermediate digital readiness), and 0.96 for class 3 (high digital readiness)) (Fig. 2 ). The participants with low digital readiness were older and predominantly female, whereas those with high readiness had higher BMIs, more bilateral symptoms, less pain, and fewer comorbidities. Hypertension, back pain, and hypercholesterolemia were the most prevalent comorbidities, which differed significantly by readiness profile. Despite slightly greater sedentary behavior, the high-readiness profile had better quality of life and performance-based test results (Table 1 ). Factors associated with the digital readiness profiles Older individuals showed significantly lower digital readiness, whereas higher education corresponded with greater readiness. Men were more often in the high readiness profile than women were. Living alone and having lower self-reported quality of life were more common in the low-readiness profile than in the high-readiness profile. A greater number of comorbidities were observed in the low profile than in both the intermediate and high profiles. Sedentary behavior was associated with greater readiness, as was obesity, but only in relation to the intermediate profile. The functional performance tests had limited influence, although a higher walking speed was less likely in the intermediate profile than in the high profile. See Table 2 . Table 2 Multinomial logistic regression analyses of the digital readiness profiles and sociodemographic characteristics, health outcomes, and functional performance-based test outcomes Low digital readiness profile vs. high (ref. high digital readiness profile) Intermediate digital readiness profile vs. high (ref. high digital readiness profile) Low digital readiness profile vs. intermediate (ref. intermediate digital readiness profile) Variable OR (95%CI) p value* OR (95% CI) p value* OR (95% CI) p value* Age at first visit (per 10-year increase) 1.96 (1.71–2.24) < 0.001 1.30 (1.18–1.44) < 0.001 1.50 (1.32–1.71) < 0.001 Sex, male (ref. female) 0.72 (0.57–0.90) 0.004 0.65 (0.55–0.78) < 0.001 1.10 (0.89–1.36) 0.381 Born in Denmark (ref. yes) 0.85 (0.43–1.65) 0.632 1.12 (0.69–1.82) 0.649 0.76 (0.40–1.42) 0.388 Danish citizenship (ref. yes) 0.59 (0.20–1.73) 0.338 0.58 (0.27–1.24) 0.160 1.02 (0.35–2.95) 0.975 Education level (ref. no, primary, or lower secondary education) < 0.001 < 0.001 0.004 Upper secondary education 0.62 (0.45–0.88) 0.86 (0.63–1.16) 0.73 (0.55–0.98) Higher education 0.33 (0.23–0.47) 0.55 (0.40–0.75) 0.60 (0.44–0.81) Cohabitate status, living alone (ref. living with others) 1.39 (1.11–1.76) 0.005 1. 13 (0.93–1.37) 0.211 1.23 (1.00-1.52) 0.052 BMI category (ref. Underweight/normal weight)** 0.090 0.047 0.806 Preobese 0.83 (0.64–1.07) 0.90 (0.73–1.11) 0.92 (0.73–1.17) Obese (class I, II, or III) 0.73 (0.54–0.97) 0.75 (0.60–0.95) 0.96 (0.74–1.26) Self-reported most affected joint, hip (ref. knee) 0.98 (0.78–1.22) 0.848 0.96 (0.80–1.14) 0.619 1.02 (0.83–1.26) 0.828 Bilateral symptoms (ref. no) 0.91 (0.73–1.13) 0.388 0.92 (0.77–1.09) 0.319 0.99 (0.81–1.22) 0.935 Symptoms length of most affected joint in months 1.00 (1.00–1.00) 0.313 1.00 (1.00–1.00) 0.607 1.00 (1.00–1.00) 0.527 Hip or knee pain during the last week (per 10-point increase) 0.96 (0.90–1.02) 0.214 0.98 (0.93–1.03) 0.371 0.98 (0.93–1.04) 0.568 Taking pain medications (ref. no) 0.88 (0.70–1.11) 0.288 0.96 (0.80–1.15) 0.678 0.92 (0.74–1.14) 0.253 Number of comorbidities 1.10 (1.04–1.17) 0.001 1.01 (0.96–1.06) 0.676 1.09 (1.02–1.15) 0.002 Compliant with WHO's minimum recommendations for physical activity (ref. noncompliant) 1.11 (0.89–1.38) 0.345 0.99 (0.82–1.17) 0.868 1.13 (0.92–1.38) 0.713 Nonsedentary behavior (ref. sedentary (sitting > 9 h/day)*** 1.63 (1.32–2.02) < 0.001 1.21 (1.02–1.42) 0.024 1.35 (1.10–1.65) 0.019 KOOS 12/HOOS 12 summary score (per 10-point increase) 0.96 (0.87–1.07) 0.463 0.98 (0.90–1.06) 0.611 0.98 (0.89–1.08) 0.722 EQ-5D VAS (per 10-point increase) 0.92 (0.87–0.98) 0.011 0.97 (0.92–1.03) 0.283 0.95 (0.90-1.00) 0.072 Number of stands (30-second chair stand test) 0.97 (0.94-1.00) 0.047 0.99 (0.96–1.01) 0.245 0.98 (0.94–1.01) 0.251 Walking speed meters per second (40-meter walk test) 0.79 (0.53–1.16) 0.228 0.72 (0.53–0.97) 0.031 1.10 (0.76–1.60) 0.623 BMI = Body Mass Index; CI = Confidence Interval; EQ-5D = EuroQol-5 Dimension; HOOS = Hip disability and Osteoarthritis Outcome Score; KOOS = Knee injury and Osteoarthritis Outcome Score; OR = Odds Ratio; Ref. = Reference category; SD = Standard Deviation; VAS = Visual Analog Scale; WHO = World Health Organization *p value of a type-3 test for the overall effect **n = 47 participants had missing BMI data ***11 participants were excluded from the analysis due to missing data or sedentary time limits Confounder analyses indicated that sex and BMI modestly influenced the associations, but the interactions were nonsignificant (Supplementary Tables 11 to 14). In contrast, higher education emerged as a confounder, as it attenuates the age effect on digital readiness and substantially reduces the likelihood of belonging to low or intermediate digital readiness groups compared with high (Supplementary Tables 14 to 17). Discussion This study is the first to assess digital readiness in individuals with hip or knee OA, revealing sociodemographic and health-related factors associated with digital readiness levels. On average, the participants had moderate digital readiness scores that varied widely. The participants had moderate, widely varying scores, with approximately half agreeing to be proficient in internet and app use, yet only a quarter tracking their lifestyle digitally and a third acknowledging increased efficiency from internet technologies. We identified three distinct digital readiness profiles: low, intermediate, and high, differing by age, sex, educational level, number of comorbidities, cohabitation status, overall self-reported health, sedentary behavior, and walking speed, with older age and lower education being more prominent factors for low readiness. These findings suggest that factors that enhance digital readiness and address digital disparities in OA care should be considered in optimizing the uptake and use of digital health interventions in clinical practice. The apparent low utilization rate of digital devices for lifestyle tracking in the study population could indicate lower digital readiness or specifically be related to devices or apps. This should be further investigated, as we did not measure the actual uptake of digital solutions. However, a study that assessed eHealth readiness among 2602 older adults reported that while more than half of the participants could find health information online, very few used health-related apps (35). This could be due to known barriers [ 3 ]. However, many existing apps simply lack the quality to facilitate effective behavior change, particularly in OA [ 45 ]. Additionally, language barriers and financial constraints imposed by paywalls may further impede the use of apps for tracking and managing lifestyle behaviors in this population [ 45 ]. This calls for improved design to enhance app engagement in OA management. Although few studies have specifically examined digital readiness profiles, some of our findings align with existing literature. In a study on individuals with cancer, age, educational attainment, cohabitation status, number of comorbidities, and physical activity levels varied significantly across digital readiness profiles [ 46 ]. Those with lower readiness were typically older, less educated, living alone, and managing multiple chronic conditions, consistent with our own results. A Norwegian study of older adults receiving home care identified the least digitally ready group as older, less educated, and with minimal access to digital tools [ 47 ]. Although some participants showed potential to benefit from digital health solutions, many required significant support or preferred non-digital alternatives [ 47 ]. In contrast, a study of people with diabetes identified no clear differences in sociodemographic or health characteristics across profiles. However, specific subgroups, such as younger individuals with mental health challenges and older adults with limited digital experience, were identified as having notably lower digital readiness [ 48 ]. Interestingly, a study of patients with implantable cardioverter-defibrillators reported lower readiness among younger individuals [ 49 ], which suggests that the nature of the health condition may influence digital engagement in different ways. These findings support our findings that age and education level are key determinants of digital readiness, though the influence of condition-specific factors should not be overlooked. Other studies exploring digital readiness in chronic conditions have relied on study-specific measures or assessed readiness based on general internet access and usage, rather than identifying readiness profiles. For instance, a large general population and diabetes sample assessed readiness using a single Likert-scale item, “I am not ready for eHealth,” and found that nearly half of the 2,895 participants reported low readiness, with even higher proportions (76%) among people with diabetes [426]. In a heart failure population, readiness to use the internet was assessed using a transtheoretical model-based staging tool using study-specific readiness items. They reported that only 23% were active online users, yet 44% of non-users expressed willingness to adopt eHealth with proper access and support, highlighting the role of external barriers rather than intrinsic resistance [ 50 ]. However, these studies focused on readiness for digital medical management and not targeting lifestyle changes. Broader investigations into digital inequalities have similarly identified key factors, such as age, education, and socioeconomic status, as strong predictors of access and engagement with digital technologies. For example, a study across 28 European member states found that these factors influenced the use of e-services, mobile apps, and social networks, reinforcing the relevance of our findings in a wider digital health context [ 51 ]. For age, they reported that younger generations were more digitally engaged, particularly with social networks, with age as the primary factor for inequality. Additionally, Gorden et al . reported that although most seniors could access the internet from home, either independently or with assistance, this ability was significantly lower with each 5-year increase in age[ 52 ]. Similarly, we observed that older individuals were likely to exhibit low digital readiness. For educational attainment, Elena-Bucea et al . also reported that higher education increased e-service use[ 51 ]. Our results supported this finding, with a strong gradient across all three profiles, with higher education associated with greater digital readiness. However, only the highest education level significantly differed between the intermediate and high profiles. This suggests that although there may be a relationship between education and digital readiness, this could indicate a segment of the OA population with some digital engagement but insufficient skills or confidence to be highly digitally ready. In our study, males were more often in the high-readiness profile than the low-readiness profile. However, there was no significant difference between the low- and intermediate-readiness profiles. Elena-Buce et al . reported minimal differences between the sexes in e-services and social network use, but males tended to use them more [ 51 ]. Differences in preferences for or access to digital tools between the sexes may exist. Intriguingly, our results also revealed that obesity and sedentary behavior were associated with high digital readiness, whereas walking speed increased the likelihood of being in the high profile. Other studies have pointed to a relationship between higher education and prolonged sitting and reported that higher education was associated with more total sitting time and less nonwork sitting, possibly due to greater leisure-time physical activity engagement [ 53 , 54 ]. This may be due to the population being resourceful despite having a higher BMI and sedentary behavior. A higher BMI in the high-readiness profile may also reflect differences in the proportions of males and females. Although we found no signs of interaction on the basis of sex, we cannot rule out confounding factors. Our findings may reflect a chance finding or unidentified factors, and further research is needed to investigate the underlying mechanisms involved. Our findings showed that age, sex, and education significantly influence higher digital readiness levels, a pattern that likely extends beyond individuals with OA and applies broadly across different populations. Our study suggests that having more chronic conditions is associated with low and intermediate digital readiness. This is particularly relevant for older adults, who are more susceptible to developing chronic conditions. Managing multiple chronic conditions [ 55 ] often involves complex healthcare needs [ 56 ], which may amplify the challenges of using digital health for lifestyle changes. Social support has been highlighted as a key facilitator of digital use, and studies have shown that support can positively shape attitudes and behaviors toward technology [ 57 , 58 ]. This may shed light on our finding that living alone increases the likelihood of being in the low profile, but it does not explain why we did not find the same for the intermediate vs. the high profile. Barriers such as motivation, attitudes, physical limitations, cognitive ability, and technological knowledge and skills have been identified for digital nonuse and initial adoption among older adults [ 57 ]. To some extent, these barriers align with our results, as declines in physical and cognitive abilities are often linked to age and comorbidity burden, and technological skills could be related to education level. Therefore, the number of chronic conditions should be considered when designing and implementing digital health interventions, which could impact an individual's ability to adopt and effectively use these solutions, especially considering that age and education level are essential for high digital readiness and, hence, the uptake of the digital health solution. Notably, Nelligan et al . reported that comorbidities did not moderate outcomes in a digital hip and knee OA exercise program (46). This suggests equal benefits if barriers to the use of digital health solutions are addressed and if the solutions are tailored to specific needs. Strengths and limitations A strength of this study is that we included 3,555 individuals, which provides high power for the latent class analysis, capturing digital readiness profiles and related characteristics of individuals with hip or knee OA motivated to change their lifestyle. This real-world clinical relevance provides insights into digital readiness in OA management; however, selecting individuals from an exercise therapy and education program in a high-income country with high technology and healthcare access may limit its generalizability to a broader population. The cross-sectional design restricts causality, and the reliance on self-reported data introduces potential recall and response biases. Potential selection bias also exists due to the online collection of questionnaires, as participants may be more digitally competent. Physical activity was measured categorically, limiting precision, and sedentary behavior was simplified by truncating high values, which may obscure subtle variations. Future studies should use objective measures for greater accuracy. Despite not undergoing a complete psychometric evaluation, the eHealth Readiness Scale demonstrated high reliability, and we improved the translation with patient-partner feedback. Furthermore, we examined digital readiness profiles as a foundation for understanding digital adoption, but we did not evaluate whether digital readiness is associated with actual uptake. Future research should assess whether digital readiness, as defined by the eHealth Readiness Scale, directly correlates with the adaptation and utilization of digital solutions. Subjectivity in interpreting latent class analysis results may have led to the identification of a suboptimal number of classes, which will require validation in an independent sample. Birthplace and citizenship may not fully capture sociodemographic disparities. Future studies should assess digital readiness across diverse populations and settings, incorporating more accurate sociodemographic and socioeconomic factors. We assessed confounders, and although we found only limited signs of interaction, we cannot exclude the influence of confounders on the results. Implication Our findings are relevant for individuals with hip or knee OA, as effective self-management and adherence to treatment plans are crucial for managing symptoms and improving quality of life. Healthcare providers can use the identified digital readiness profiles to tailor health interventions. For example, older adults or those with lower education levels may face more barriers to using digital technologies. While these factors may influence the initial uptake of digital solutions, research suggests that they have less impact on outcomes once they are adopted [ 59 – 61 ]. Furthermore, individuals who are initially hesitant due to low confidence in using technology often report positive experiences once they engage with digital interventions [ 62 ]. By recognizing these dynamics, clinicians may tailor interventions, offering support or alternative in-person solutions to ensure that all individuals, regardless of their digital readiness, may access health interventions. The lack of research into digital readiness among individuals with hip or knee OA highlights the unique contribution of our study and underscores the need for further investigation. The identified digital readiness profiles can guide future studies in developing and validating tailored digital health interventions. Screening for digital readiness via tools such as the eHealth Readiness Scale may help identify individuals (un)prepared to engage with digital health solutions, addressing potential digital disparities [ 27 ]. For comparative reasons, a clear definition and alignment of measurement tools is a research area for future studies, as existing studies use different methods to define and measure digital readiness, limiting direct comparisons [ 27 , 63 ]. Additionally, research should employ longitudinal designs to understand how digital readiness evolves and what influences it, as well as identify effective enhancement strategies. Conclusion Our study revealed moderate digital readiness among individuals with hip or knee OA and low agreement on digital solution efficiency and utilization. We identified three digital readiness profiles distinguished by age, sex, education level, and number of comorbidities. This underscores the importance of considering digital readiness levels when designing and implementing digital health interventions to reduce digital disparities. Our results demonstrate the necessity of taking additional measures when introducing digital health solutions, emphasizing that digital options should be one of several health intervention strategies. In-person interventions remain essential for rehabilitation or exercise for those who are not ready to use digital health solutions. Therefore, the focus should be on a person-centered approach, stratifying health initiatives to meet diverse needs and allocate healthcare more sustainably. Declarations Ethical approval for GLA:D® was waived by the North Denmark Region's ethics committee. The GLA:D® registry is approved by the Danish Data Protection Agency (SDU; 10.084, 11.847). Under the Danish Data Protection Act, patient consent was not needed, as data were used for research and statistical purposes. This study adheres to the Helsinki Declaration. Consent for publication Not applicable Availability of data and materials The datasets used and analyzed in the current study are available for research purposes upon reasonable request. Contact Ewa M. Roos [email protected] or Søren T. Skou [email protected] . Author Contributions Statement GZ, LHT, LT, DGT, EMR, and STS designed and conceptualized the study. GZ, STS, EMR, and DGT contributed to the acquisition of data. GZ conducted the analysis and drafted the manuscript. STS provided senior supervision by interpreting findings and critically revising the manuscript. LHT, LT, DGT, and EM contributed with substantive manuscript revisions. All authors reviewed and approved the final manuscript, agreed to be accountable for their own contributions, and ensured the integrity and accuracy of the work. Competing interests EMR is the copyright holder of the KOOS and several other patient-reported outcome measures and cofounders of GLA:D®, a not-for-profit initiative to implement clinical guidelines in primary care hosted by the University of Southern Denmark. STS has received personal fees from Munksgaard, TrustMe-Ed, and Nestlé Health Science outside the submitted work and is cofounder of GLA:D®. Funding The initiation of GLA:D ® was partly funded by the Danish Physiotherapy Association's fund for research, education, and practice development; the Danish Rheumatism Association; and the Physiotherapy Practice Foundation. This study received funding from Region Zealand (Exercise First), the NSR Research Fund (no. A447), and a one-year PhD faculty scholarship from the University of Southern Denmark. The funders had no role in the study design, data collection, analysis, interpretation, report writing, or submission. 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Grønne","email":"","orcid":"","institution":"Naestved-Slagelse-Ringsted Hospital, Region Zealand","correspondingAuthor":false,"prefix":"","firstName":"Dorte","middleName":"T.","lastName":"Grønne","suffix":""},{"id":435443873,"identity":"6ddf1cfe-5893-46b8-b1ea-ae0be1be75be","order_by":2,"name":"Lars H. Tang","email":"","orcid":"","institution":"Naestved-Slagelse-Ringsted Hospital, Region Zealand","correspondingAuthor":false,"prefix":"","firstName":"Lars","middleName":"H.","lastName":"Tang","suffix":""},{"id":435443874,"identity":"a25bd2d1-cbf2-491f-b644-503d84ec7552","order_by":3,"name":"Lau C. Thygesen","email":"","orcid":"","institution":"University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Lau","middleName":"C.","lastName":"Thygesen","suffix":""},{"id":435443875,"identity":"e332ae2e-86e4-41b5-9a63-8796b66f02b5","order_by":4,"name":"Ewa M. Roos","email":"","orcid":"","institution":"University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Ewa","middleName":"M.","lastName":"Roos","suffix":""},{"id":435443876,"identity":"7f64900b-669c-4e77-ad3d-78c8b94aa310","order_by":5,"name":"Søren T. Skou","email":"","orcid":"","institution":"Naestved-Slagelse-Ringsted Hospital, Region Zealand","correspondingAuthor":false,"prefix":"","firstName":"Søren","middleName":"T.","lastName":"Skou","suffix":""}],"badges":[],"createdAt":"2025-03-26 12:08:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6312226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6312226/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1002/msc.70127","type":"published","date":"2025-06-09T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80047967,"identity":"cae9f343-91b0-421d-8309-8f7c1de2222a","added_by":"auto","created_at":"2025-04-07 10:00:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe eHealth Readiness Scale item response distribution in percentage\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6312226/v1/059fdb25aa93f443574eed07.png"},{"id":80046578,"identity":"333b9c18-205d-46ab-a012-ffb0b4def012","added_by":"auto","created_at":"2025-04-07 09:52:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":326529,"visible":true,"origin":"","legend":"\u003cp\u003eThe latent class analysis of the 3-class model with the probabilities of the profiles within the collapsed response categories\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6312226/v1/17529db03d55d3bac554a0af.png"},{"id":84575131,"identity":"022c51bf-2e79-46a9-a598-ff6b0f0f7788","added_by":"auto","created_at":"2025-06-13 16:36:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2869765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6312226/v1/586a11e2-9e44-4aab-8c56-de6dbcdbd4c1.pdf"},{"id":80046574,"identity":"1a0e2374-8001-4753-8bd3-c770b99d8db2","added_by":"auto","created_at":"2025-04-07 09:52:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":69298,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialARTv1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6312226/v1/348cec7959e98b4e1fc1ea84.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDigital readiness among 3,555 individuals with hip or knee osteoarthritis initiating a supervised education and exercise therapy program: a cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe high and increasing prevalence of hip and knee osteoarthritis (OA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] highlights the importance of innovative approaches to disease management, including the integration of digital health solutions. Telehealth platforms, smartphone applications (apps), and wearable devices can provide continuous personalized support and care, monitor symptoms, and facilitate remote communication, which is essential for managing chronic conditions such as hip and knee OA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Digital solutions further offer benefits such as reduced travel, shorter waiting times, lower costs, greater flexibility, and intensified and practical care [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Adapting to digital health solutions is becoming increasingly important in healthcare [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and embracing digital health solutions in hip or knee OA management represents a proactive approach to mitigating the long-term impacts of OA on both the individual and healthcare systems [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigitally delivered exercise programs are particularly intriguing, as they offer an alternative to traditional rehabilitation for individuals with hip and knee OA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], especially for those with severe functional limitations or commitments that hinder in-person participation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, common barriers to adoption include privacy concerns, reduced personal interaction, and doubts about the credibility of digital solutions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, a persistent digital divide affects older adults, individuals with low literacy, and those from socioeconomically disadvantaged backgrounds, who may face challenges in accessing and using digital technologies effectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These disparities highlight that not all are equally ready to engage with digital health solutions.\u003c/p\u003e \u003cp\u003eWhile existing studies have demonstrated several health benefits of digitally delivered exercise for individuals with hip or knee OA [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], a knowledge gap remains in understanding how ready individuals with hip or knee OA are to start engaging with these digital health solutions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This can be determined by measuring digital readiness, which refers to the combination of motivation, willingness, confidence, and capability needed to initially accept and engage with digital health solutions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Various frameworks have explored readiness to use digital health, suggesting that diverse personal, social, and contextual factors influence digital readiness [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While closely related to digital health literacy\u0026mdash;the ability to find, understand, and use health information via technology [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u0026mdash;digital readiness may extend beyond this by incorporating emotional and social dimensions of use [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Using the eHealth Readiness Scale level to assess digital readiness, self-efficacy (a person's confidence in the ability to use digital solutions) is emphasized as a central component. The eHealth Readiness scale provides a low-burden measurement tool that accounts for both the psychological and practical aspects of engaging with digital health solutions.\u003c/p\u003e \u003cp\u003eUnderstanding digital readiness is essential for optimizing the implementation and uptake of digital health interventions, as users must go beyond the first access to meaningfully engage with these solutions to succeed. Furthermore, identifying and stratifying individuals based on digital readiness and related factors may enable more tailored and inclusive intervention strategies, ensuring that digital health interventions are accessible, effective, and aligned with user needs. Digital readiness can, therefore, be a pivotal aspect of maximizing potential benefits and serves as a foundation for the successful design, implementation, and sustainability of digital health solutions [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This study, therefore, aims to investigate digital readiness profiles among people with hip or knee OA initiating in-person physiotherapist-supervised exercise therapy and education in primary care (GLA:D)\u0026reg; and assess associations with sociodemographic and health characteristics and performance-based functional test outcomes with the established digital readiness profiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The STROBE checklist can be found in Supplementary Table\u0026nbsp;1. The statistical analysis plan was preregistered before analysis and is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.osf.io/sbpw4\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and data sources\u003c/h2\u003e\n \u003cp\u003eThis cross-sectional study uses national GLA:D\u0026reg; registry data. GLA:D\u0026reg; offers guideline-aligned group education and supervised exercise therapy for people with hip or knee OA [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] through 2\u0026ndash;3 90-minute (min) education sessions and 12 60-min supervised neuromuscular exercise sessions delivered twice weekly for six weeks. Approximately 6,000 participants initiate GLA:D\u0026reg; annually through self-referral or from a general practitioner at one of the nearly 300 Danish physiotherapy clinics, either private (with ~\u0026thinsp;40% public reimbursement) or public (free attendance).\u003c/p\u003e\n \u003cp\u003eThe GLA:D\u0026reg; registry collects data from therapists and participants at baseline, three months, and 12 months after baseline; only baseline data are used in this study. Participants received an emailed link to an online questionnaire with up to two reminders after one week, and onsite kiosk options were available for participants without email.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eParticipants who initiated GLA:D\u0026reg; between March 7, 2022, and January 5, 2023, were included, as digital readiness information was collected during this period. The inclusion criterion in GLA:D\u0026reg; is a clinical hip or knee OA diagnosis. The exclusion criteria are reasons other than OA for joint problems (e.g., tumor, inflammatory joint disease, or sequelae after a hip fracture), having other competing and more severe symptoms than OA problems (e.g., chronic, generalized pain, or fibromyalgia), and being unable to read and understand Danish. Radiographic images are not needed for a clinical diagnosis of OA[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] and, therefore, are not a criterion for entering the GLA:D\u0026reg;.\u003c/p\u003e\n\u003ch3\u003eVariables of interest\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eDigital readiness\u003c/h2\u003e\n \u003cp\u003eThe eHealth Readiness Scale was developed in 2016 by Bhalla \u003cem\u003eet al\u003c/em\u003e. to measure participants\u0026apos; readiness to engage in eHealth or digital health interventions (digital readiness) [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The scale items are based on Bandura\u0026apos;s theory on self-efficacy, previous literature, and measurement scales [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The 7-item scale is scored on a 6-point Likert-type scale (1\u0026thinsp;=\u0026thinsp;strongly disagree and 6\u0026thinsp;=\u0026thinsp;strongly agree), and scores range from 7 to 42, with higher scores indicating greater readiness with no presiding thresholds of (in)sufficient readiness [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The scale has previously demonstrated good psychometric properties, with a robust unidimensional scale and high internal consistency (Cronbach\u0026apos;s alpha 0.81) [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. While Bhalla \u003cem\u003eet al\u003c/em\u003e. assessed digital readiness in the continuation of a digital health intervention [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], we assessed baseline readiness without referencing a specific digital solution or measuring uptake in this current study.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLinguistic and cross-cultural validation\u003c/h3\u003e\n\u003cp\u003eThe eHealth Readiness Scale, including the introduction text, was independently forward and backward translated from English to Danish by two health professionals (one native Danish speaker fluent in English and one native English speaker fluent in Danish). Dr. Bhalla approved the use and translation of the scale. Two patient-partners reviewed and commented on the Danish translation. A third researcher resolved discrepancies, incorporating patient feedback to finalize the Danish version (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDigital readiness profiles\u003c/h2\u003e\n \u003cp\u003eAs the eHealth Readiness Scale has no approved cutoff scores for readiness levels, an explorative approach was chosen using latent class analysis to identify subgroups on the basis of the participant responses to the scale items.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSociodemographic characteristics\u003c/h3\u003e\n\u003cp\u003eAge and sex were derived from the Danish Civil Registration (CPR) System [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], with age continuously calculated from the initial visit date. Sex was recorded as male/female. We recorded whether participants were born in Denmark and had Danish citizenship as binary variables (yes/no). Birthplace and citizenship were included as proxies for language proficiency and cultural integration. Education level was based on the highest level completed and categorized into no, primary, or lower secondary (collapsed), upper secondary, and higher education. Cohabitation status was recorded, indicating whether the participant lived alone or with others.\u003c/p\u003e\n\u003ch3\u003eHealth characteristics\u003c/h3\u003e\n\u003cp\u003eBody mass index (BMI) was calculated on the basis of body weight in kilograms and height in centimeters (measured by the therapist), which was subsequently categorized into \u0026lt;\u0026thinsp;18.4 to 24.9 for underweight and normal weight, 25.0 to 29.9 for preobese, and \u0026ge;\u0026thinsp;30 for obese classes I to III [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The participants indicated the most affected (symptomatic) hip or knee joint. Therapists recorded the duration of symptoms in months for the most affected joint. The pain intensity (of the most affected joint) during the last week was measured via the visual analog scale (VAS) [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], which ranges from 0-100 mm (indicating \u0026apos;no pain\u0026apos; to \u0026apos;maximum pain\u0026apos;). Therapists recorded analgesic use, including acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioid medication, over the preceding two weeks. Comorbidities were assessed by quantifying the number of self-reported health conditions from 30 possibilities.\u003c/p\u003e\n\u003cp\u003eModerate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) were based on self-reported time spent engaging in either intensity on a typical week (MVPA: \u0026lt;30 min, 30\u0026ndash;89 min, 90\u0026ndash;149 min, 150\u0026ndash;299 min, or \u0026gt;\u0026thinsp;300 min; VPA: \u0026lt;30 min, 30\u0026ndash;59 min, 60\u0026ndash;89 min, 90\u0026ndash;149 min, or \u0026gt;\u0026thinsp;150 min). Physical activity compliance was determined in a binary manner, adhering to the WHO minimum recommendations for adult physical activity (\u0026ge;\u0026thinsp;150 min of moderate physical activity (MPA), \u0026ge;\u0026thinsp;75 min of VPA, or an equivalent combination). This calculation was similar to the Nordic Physical Activity Questionnaire-short validation method [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Sedentary behavior was calculated by typically daily sitting time during transportation, work/school, leisure/screen time, or other, categorized with a threshold of \u0026ge;\u0026thinsp;9 hours per day indicating sedentary behavior [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Values\u0026thinsp;\u0026gt;\u0026thinsp;16 hours for work/school, \u0026gt;\u0026thinsp;6 hours for other sedentary behaviors, or \u0026gt;\u0026thinsp;24 hours were excluded.\u003c/p\u003e\n\u003cp\u003eThe summary scores of the Hip disability and Osteoarthritis Outcome Score 12 (HOOS-12) and the Knee disability and Osteoarthritis Outcome Score 12 (KOOS-12) questionnaires were utilized to assess pain, function, and quality of life. Scores are given on a scale of 0-100 (higher scores indicate better quality of life) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. The EuroQol 5-Dimensions (EQ-5D) 5-Level assesses health-related quality of life across five dimensions, with five response levels in an index score. The index scores were based on the Danish value set [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, the EQ-5D VAS scores overall self-rated health from 0-100 (higher scores indicate better quality of life) [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional performance-based tests\u003c/h2\u003e\n \u003cp\u003eFunctional performance was assessed using the 30-second chair-stand test and the 40-meter fast-paced walk test, as recommended by the Osteoarthritis Research Society International (OARSI) [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. Both tests were administered once at the respective clinics according to OARSI protocols [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The chair-stand test recorded the number of completed stands in 30 seconds, while the walk test measured walking speed (m/s) over 40 meters. The use of walking aids during testing was documented.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eNonresponders\u003c/h2\u003e\n \u003cp\u003eA nonresponder analysis was performed for the available items: age, sex, BMI, most affected joint, analgesic use, results from the 30-second chair-stand test and the 40-meter fast-paced walk test, as well as available email contact.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics were computed to summarize participants\u0026apos; sociodemographic and health characteristics, performance-based test results, and responses to the eHealth Readiness Scale items. Categorical data are presented as absolute frequencies and percentages, whereas continuous data are expressed as the means with standard deviations (SDs) or medians (IQRs) as appropriate. Scale reliability was tested via Cronbach\u0026apos;s alpha, interitem, and item-rest tests for internal consistency.\u003c/p\u003e\n \u003cp\u003eLatent class analysis was performed via Mplus 8.10[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Latent class analysis is a probabilistic model-based technique used to categorize a sample into distinct and exhaustive subgroups according to their response pattern to the eHealth Readiness Scale [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Models with one to five classes were assessed, including models where the response categories were collapsed for similar answer categories (i.e., strongly agree with agree, mildly agree with mildly disagree, and strongly disagree with disagree). The final number of classes was determined on the basis of conceptual meaning, subgroup size, and entropy statistics, indicating the certainty of subgroup membership [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Maximum likelihood estimation via the expectation-maximization procedure was employed with automatically generated random starting values and 1,000 iterations to enhance generalizability. Entropy statistics were evaluated at a threshold of \u0026ge;\u0026thinsp;0.80 to ascertain the optimal number of latent classes.\u003c/p\u003e\n \u003cp\u003eMultinomial logistic regressions followed class determination and examined associations between digital readiness profiles (categorical outcome) and selected sociodemographic, health, and performance-based variables (exposures) in a single model. One readiness profile was set as the base reference. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Age was rescaled by 10 to reflect a decade-level increase; VAS pain, KOOS-12/HOOS-12, and EQ-5D VAS scores were similarly rescaled to reflect a 10-point decrease. Only the EQ-5D VAS was included in the multinomial logistic regressions to avoid multicollinearity. Multicollinearity was assessed via variance inflation factors (VIF) from linear regression using the same predictors. All VIFs were below 3.1; see Supplementary Table\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003ePotential confounders were assessed by adding age, sex, BMI, education, and sedentary behavior stepwise to unadjusted multinomial logistic models to examine changes in odds ratios. Interaction effects between age, sex, BMI, and education were tested using models with interaction terms to assess whether associations with digital readiness profiles varied by combined characteristics.\u003c/p\u003e\n \u003cp\u003eThe data were analyzed via STATA version 18 [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eA total of 3,555 out of 4,776 participants responded to the survey (74%) and were included in the study. The participants were primarily older adults (mean age 66.4 years, SD 9.6), with a majority being female (67%) and the knee being the most affected joint (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and health characteristics and performance-based functional test of the total group and the three digital readiness profiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Digital Readiness Profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate Digital Readiness Profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh Digital Readiness Profile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParticipants, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1528 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1287 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first visit, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.4 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.8 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2386 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e514 (69.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1083 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e789 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBorn in Denmark, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3414 (96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e716 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1470 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1228 (95.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDanish citizenship, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3500 (98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e733 (99.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1509 (98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1258 (97.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, primary, or lower secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1675 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e761 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e554 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1504 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e603 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e649 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohabitate status, living alone, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e945 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e280 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.7 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.0 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI category, n (%)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight/normal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e942 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreobese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1351 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (class I, II, or III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1215 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e509 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e468 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported most affected joint, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2362 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e485 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1022 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e855 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1193 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e506 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e432 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilateral symptoms, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1350 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555 (36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e532 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms length of most affected joint in months, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0 (6.0\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (6.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (5.0\u0026ndash;24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0 (5.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHip or knee pain during the last week (0-100), mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.0 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.3 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.1 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.0 (22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaking pain medications, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2277 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e802 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of comorbidities, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThree most common comorbidities, n (%)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1525 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e382 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e637 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e506 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1092 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1083 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate to vigorous physical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e783 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e261 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;89 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1024 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;149 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u0026ndash;299 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e724 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e264 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;300 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNoncompliant with WHO's minimum recommendations for physical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2389 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1044 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSedentary behavior (sitting\u0026thinsp;\u0026gt;\u0026thinsp;9 h/day), n (%)***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1633 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e607 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e595 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeHealth Readiness Scale score, range 7\u0026ndash;42, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKOOS/HOOS 12 summary score, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.5 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.0 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.1 (14.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEQ-5D index, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758 (0.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743 (0.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.754 (0.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.772 (0.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEQ-5D VAS, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.4 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.1 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.3 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.7 (18.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of chair stands (30 seconds chair stand test), mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWalking speed in meters per second (40 meters walk test), mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55 (0.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; EQ-5D\u0026thinsp;=\u0026thinsp;EuroQol 5-Dimension 5-Level; HOOS\u0026thinsp;=\u0026thinsp;Hip disability and Osteoarthritis Outcome Score; IQR\u0026thinsp;=\u0026thinsp;Interquartile range; KOOS\u0026thinsp;=\u0026thinsp;Knee injury and Osteoarthritis Outcome Score; SD\u0026thinsp;=\u0026thinsp;Standard deviation; VAS\u0026thinsp;=\u0026thinsp;Visual Analog Scale; WHO\u0026thinsp;=\u0026thinsp;World Health Organization.\u003c/p\u003e \u003cp\u003e*n\u0026thinsp;=\u0026thinsp;47 participants had missing BMI data\u003c/p\u003e \u003cp\u003e**Differences in prevalence of the three most common comorbidities across digital readiness profiles assessed by Chi-square test (χ\u0026sup2;) and found significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.007)\u003c/p\u003e \u003cp\u003e***n\u0026thinsp;=\u0026thinsp;11 participants were excluded from the analysis due to missing data or sedentary time limits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNonresponders\u003c/h2\u003e \u003cp\u003eThe 1,221 nonresponders were older (mean age 67.3 years, SD 11.0) and more often lacked an email contact (8.7%). The nonresponders also had the knee as the most symptomatic joint but performed worse on the functional performance-based tests than the responders (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDigital readiness\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eReliability of the eHealth Readiness Scale\u003c/h2\u003e \u003cp\u003eThe Cronbach's alpha of the eHealth Readiness Scale was 0.90, with high item‒test scores (range 0.72\u0026ndash;0.85) and average interitem covariance (1.20) (Supplementary Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eeHealth Readiness responses\u003c/h2\u003e \u003cp\u003eThe mean eHealth Readiness Scale score was 25.9 (SD 8.1). More than half of the participants (53.0%) agreed or strongly agreed that they could make good use of the internet, web apps, or apps (item 4), whereas 21.4% disagreed or strongly disagreed. However, only 32.4% agreed or strongly agreed that internet technologies made them more efficient (item 3), and 26.3% of the participants agreed or strongly agreed to use an internet-connected device to track their lifestyle (item 7), whereas 46.8% disagreed or strongly disagreed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDigital readiness profiles\u003c/h2\u003e \u003cp\u003eFive latent class analysis models were assessed. An even participant distribution within the 3-, 4-, and 5-class models (Supplementary Tables\u0026nbsp;7 to 10) was found, and all had a good statistical fit (entropy\u0026thinsp;\u0026ge;\u0026thinsp;0.87). Consequently, we opted for a simplified model comprising the three classes with collapsed response categories, as this model aligned well with the interpretability and clinical meaningfulness of the eHealth Readiness Scale. The 3-class model showed more distinct digital readiness profiles than the 4- and 5-class models did, and the mean probabilities for class membership were high (0.94 for class 1 (low digital readiness), 0.94 for class 2 (intermediate digital readiness), and 0.96 for class 3 (high digital readiness)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe participants with low digital readiness were older and predominantly female, whereas those with high readiness had higher BMIs, more bilateral symptoms, less pain, and fewer comorbidities. Hypertension, back pain, and hypercholesterolemia were the most prevalent comorbidities, which differed significantly by readiness profile. Despite slightly greater sedentary behavior, the high-readiness profile had better quality of life and performance-based test results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with the digital readiness profiles\u003c/h2\u003e \u003cp\u003eOlder individuals showed significantly lower digital readiness, whereas higher education corresponded with greater readiness. Men were more often in the high readiness profile than women were. Living alone and having lower self-reported quality of life were more common in the low-readiness profile than in the high-readiness profile. A greater number of comorbidities were observed in the low profile than in both the intermediate and high profiles. Sedentary behavior was associated with greater readiness, as was obesity, but only in relation to the intermediate profile. The functional performance tests had limited influence, although a higher walking speed was less likely in the intermediate profile than in the high profile. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression analyses of the digital readiness profiles and sociodemographic characteristics, health outcomes, and functional performance-based test outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow digital readiness profile vs. high (ref. high digital readiness profile)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIntermediate digital readiness profile vs. high (ref. high digital readiness profile)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLow digital readiness profile vs. intermediate (ref. intermediate digital readiness profile)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first visit (per 10-year increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96 (1.71\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.18\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50 (1.32\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, male (ref. female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.57\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.55\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.89\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBorn in Denmark (ref. yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.43\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.69\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76 (0.40\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDanish citizenship (ref. yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.20\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.27\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.35\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level (ref. no, primary, or lower secondary education)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.45\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.63\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73 (0.55\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.23\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55 (0.40\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.44\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohabitate status, living alone (ref. living with others)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.11\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 13 (0.93\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23 (1.00-1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI category (ref. Underweight/normal weight)**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreobese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.64\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.73\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.73\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (class I, II, or III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.54\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.60\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.74\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported most affected joint, hip (ref. knee)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.78\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.80\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.83\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilateral symptoms (ref. no)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.73\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.77\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.81\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms length of most affected joint in months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHip or knee pain during the last week (per 10-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.90\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.93\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaking pain medications (ref. no)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.70\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.80\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.74\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.04\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompliant with WHO's minimum recommendations for physical activity (ref. noncompliant)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.89\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.82\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.92\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNonsedentary behavior (ref. sedentary (sitting\u0026thinsp;\u0026gt;\u0026thinsp;9 h/day)***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.32\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (1.02\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35 (1.10\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKOOS 12/HOOS 12 summary score (per 10-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.87\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.90\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.89\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEQ-5D VAS (per 10-point increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.87\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.92\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (0.90-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of stands (30-second chair stand test)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.94-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWalking speed meters per second (40-meter walk test)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.53\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.53\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.76\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; EQ-5D\u0026thinsp;=\u0026thinsp;EuroQol-5 Dimension; HOOS\u0026thinsp;=\u0026thinsp;Hip disability and Osteoarthritis Outcome Score; KOOS\u0026thinsp;=\u0026thinsp;Knee injury and Osteoarthritis Outcome Score; OR\u0026thinsp;=\u0026thinsp;Odds Ratio; Ref. = Reference category; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; VAS\u0026thinsp;=\u0026thinsp;Visual Analog Scale; WHO\u0026thinsp;=\u0026thinsp;World Health Organization\u003c/p\u003e \u003cp\u003e*p value of a type-3 test for the overall effect\u003c/p\u003e \u003cp\u003e**n\u0026thinsp;=\u0026thinsp;47 participants had missing BMI data\u003c/p\u003e \u003cp\u003e***11 participants were excluded from the analysis due to missing data or sedentary time limits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConfounder analyses indicated that sex and BMI modestly influenced the associations, but the interactions were nonsignificant (Supplementary Tables\u0026nbsp;11 to 14). In contrast, higher education emerged as a confounder, as it attenuates the age effect on digital readiness and substantially reduces the likelihood of belonging to low or intermediate digital readiness groups compared with high (Supplementary Tables\u0026nbsp;14 to 17).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to assess digital readiness in individuals with hip or knee OA, revealing sociodemographic and health-related factors associated with digital readiness levels. On average, the participants had moderate digital readiness scores that varied widely. The participants had moderate, widely varying scores, with approximately half agreeing to be proficient in internet and app use, yet only a quarter tracking their lifestyle digitally and a third acknowledging increased efficiency from internet technologies. We identified three distinct digital readiness profiles: low, intermediate, and high, differing by age, sex, educational level, number of comorbidities, cohabitation status, overall self-reported health, sedentary behavior, and walking speed, with older age and lower education being more prominent factors for low readiness. These findings suggest that factors that enhance digital readiness and address digital disparities in OA care should be considered in optimizing the uptake and use of digital health interventions in clinical practice.\u003c/p\u003e \u003cp\u003eThe apparent low utilization rate of digital devices for lifestyle tracking in the study population could indicate lower digital readiness or specifically be related to devices or apps. This should be further investigated, as we did not measure the actual uptake of digital solutions. However, a study that assessed eHealth readiness among 2602 older adults reported that while more than half of the participants could find health information online, very few used health-related apps (35). This could be due to known barriers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, many existing apps simply lack the quality to facilitate effective behavior change, particularly in OA [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, language barriers and financial constraints imposed by paywalls may further impede the use of apps for tracking and managing lifestyle behaviors in this population [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This calls for improved design to enhance app engagement in OA management.\u003c/p\u003e \u003cp\u003eAlthough few studies have specifically examined digital readiness profiles, some of our findings align with existing literature. In a study on individuals with cancer, age, educational attainment, cohabitation status, number of comorbidities, and physical activity levels varied significantly across digital readiness profiles [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Those with lower readiness were typically older, less educated, living alone, and managing multiple chronic conditions, consistent with our own results. A Norwegian study of older adults receiving home care identified the least digitally ready group as older, less educated, and with minimal access to digital tools [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although some participants showed potential to benefit from digital health solutions, many required significant support or preferred non-digital alternatives [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, a study of people with diabetes identified no clear differences in sociodemographic or health characteristics across profiles. However, specific subgroups, such as younger individuals with mental health challenges and older adults with limited digital experience, were identified as having notably lower digital readiness [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Interestingly, a study of patients with implantable cardioverter-defibrillators reported lower readiness among younger individuals [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which suggests that the nature of the health condition may influence digital engagement in different ways. These findings support our findings that age and education level are key determinants of digital readiness, though the influence of condition-specific factors should not be overlooked.\u003c/p\u003e \u003cp\u003eOther studies exploring digital readiness in chronic conditions have relied on study-specific measures or assessed readiness based on general internet access and usage, rather than identifying readiness profiles. For instance, a large general population and diabetes sample assessed readiness using a single Likert-scale item, \u0026ldquo;I am not ready for eHealth,\u0026rdquo; and found that nearly half of the 2,895 participants reported low readiness, with even higher proportions (76%) among people with diabetes [426]. In a heart failure population, readiness to use the internet was assessed using a transtheoretical model-based staging tool using study-specific readiness items. They reported that only 23% were active online users, yet 44% of non-users expressed willingness to adopt eHealth with proper access and support, highlighting the role of external barriers rather than intrinsic resistance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, these studies focused on readiness for digital medical management and not targeting lifestyle changes.\u003c/p\u003e \u003cp\u003eBroader investigations into digital inequalities have similarly identified key factors, such as age, education, and socioeconomic status, as strong predictors of access and engagement with digital technologies. For example, a study across 28 European member states found that these factors influenced the use of e-services, mobile apps, and social networks, reinforcing the relevance of our findings in a wider digital health context [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. For age, they reported that younger generations were more digitally engaged, particularly with social networks, with age as the primary factor for inequality. Additionally, Gorden \u003cem\u003eet al\u003c/em\u003e. reported that although most seniors could access the internet from home, either independently or with assistance, this ability was significantly lower with each 5-year increase in age[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similarly, we observed that older individuals were likely to exhibit low digital readiness.\u003c/p\u003e \u003cp\u003eFor educational attainment, Elena-Bucea \u003cem\u003eet al\u003c/em\u003e. also reported that higher education increased e-service use[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our results supported this finding, with a strong gradient across all three profiles, with higher education associated with greater digital readiness. However, only the highest education level significantly differed between the intermediate and high profiles. This suggests that although there may be a relationship between education and digital readiness, this could indicate a segment of the OA population with some digital engagement but insufficient skills or confidence to be highly digitally ready.\u003c/p\u003e \u003cp\u003eIn our study, males were more often in the high-readiness profile than the low-readiness profile. However, there was no significant difference between the low- and intermediate-readiness profiles. Elena-Buce \u003cem\u003eet al\u003c/em\u003e. reported minimal differences between the sexes in e-services and social network use, but males tended to use them more [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Differences in preferences for or access to digital tools between the sexes may exist.\u003c/p\u003e \u003cp\u003eIntriguingly, our results also revealed that obesity and sedentary behavior were associated with high digital readiness, whereas walking speed increased the likelihood of being in the high profile. Other studies have pointed to a relationship between higher education and prolonged sitting and reported that higher education was associated with more total sitting time and less nonwork sitting, possibly due to greater leisure-time physical activity engagement [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This may be due to the population being resourceful despite having a higher BMI and sedentary behavior. A higher BMI in the high-readiness profile may also reflect differences in the proportions of males and females. Although we found no signs of interaction on the basis of sex, we cannot rule out confounding factors. Our findings may reflect a chance finding or unidentified factors, and further research is needed to investigate the underlying mechanisms involved.\u003c/p\u003e \u003cp\u003eOur findings showed that age, sex, and education significantly influence higher digital readiness levels, a pattern that likely extends beyond individuals with OA and applies broadly across different populations. Our study suggests that having more chronic conditions is associated with low and intermediate digital readiness. This is particularly relevant for older adults, who are more susceptible to developing chronic conditions. Managing multiple chronic conditions [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] often involves complex healthcare needs [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], which may amplify the challenges of using digital health for lifestyle changes.\u003c/p\u003e \u003cp\u003eSocial support has been highlighted as a key facilitator of digital use, and studies have shown that support can positively shape attitudes and behaviors toward technology [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This may shed light on our finding that living alone increases the likelihood of being in the low profile, but it does not explain why we did not find the same for the intermediate vs. the high profile.\u003c/p\u003e \u003cp\u003eBarriers such as motivation, attitudes, physical limitations, cognitive ability, and technological knowledge and skills have been identified for digital nonuse and initial adoption among older adults [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. To some extent, these barriers align with our results, as declines in physical and cognitive abilities are often linked to age and comorbidity burden, and technological skills could be related to education level. Therefore, the number of chronic conditions should be considered when designing and implementing digital health interventions, which could impact an individual's ability to adopt and effectively use these solutions, especially considering that age and education level are essential for high digital readiness and, hence, the uptake of the digital health solution. Notably, Nelligan \u003cem\u003eet al\u003c/em\u003e. reported that comorbidities did not moderate outcomes in a digital hip and knee OA exercise program (46). This suggests equal benefits if barriers to the use of digital health solutions are addressed and if the solutions are tailored to specific needs.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA strength of this study is that we included 3,555 individuals, which provides high power for the latent class analysis, capturing digital readiness profiles and related characteristics of individuals with hip or knee OA motivated to change their lifestyle. This real-world clinical relevance provides insights into digital readiness in OA management; however, selecting individuals from an exercise therapy and education program in a high-income country with high technology and healthcare access may limit its generalizability to a broader population.\u003c/p\u003e \u003cp\u003eThe cross-sectional design restricts causality, and the reliance on self-reported data introduces potential recall and response biases. Potential selection bias also exists due to the online collection of questionnaires, as participants may be more digitally competent. Physical activity was measured categorically, limiting precision, and sedentary behavior was simplified by truncating high values, which may obscure subtle variations. Future studies should use objective measures for greater accuracy.\u003c/p\u003e \u003cp\u003eDespite not undergoing a complete psychometric evaluation, the eHealth Readiness Scale demonstrated high reliability, and we improved the translation with patient-partner feedback. Furthermore, we examined digital readiness profiles as a foundation for understanding digital adoption, but we did not evaluate whether digital readiness is associated with actual uptake. Future research should assess whether digital readiness, as defined by the eHealth Readiness Scale, directly correlates with the adaptation and utilization of digital solutions. Subjectivity in interpreting latent class analysis results may have led to the identification of a suboptimal number of classes, which will require validation in an independent sample. Birthplace and citizenship may not fully capture sociodemographic disparities. Future studies should assess digital readiness across diverse populations and settings, incorporating more accurate sociodemographic and socioeconomic factors. We assessed confounders, and although we found only limited signs of interaction, we cannot exclude the influence of confounders on the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplication\u003c/h2\u003e \u003cp\u003eOur findings are relevant for individuals with hip or knee OA, as effective self-management and adherence to treatment plans are crucial for managing symptoms and improving quality of life. Healthcare providers can use the identified digital readiness profiles to tailor health interventions. For example, older adults or those with lower education levels may face more barriers to using digital technologies. While these factors may influence the initial uptake of digital solutions, research suggests that they have less impact on outcomes once they are adopted [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Furthermore, individuals who are initially hesitant due to low confidence in using technology often report positive experiences once they engage with digital interventions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. By recognizing these dynamics, clinicians may tailor interventions, offering support or alternative in-person solutions to ensure that all individuals, regardless of their digital readiness, may access health interventions.\u003c/p\u003e \u003cp\u003eThe lack of research into digital readiness among individuals with hip or knee OA highlights the unique contribution of our study and underscores the need for further investigation. The identified digital readiness profiles can guide future studies in developing and validating tailored digital health interventions. Screening for digital readiness via tools such as the eHealth Readiness Scale may help identify individuals (un)prepared to engage with digital health solutions, addressing potential digital disparities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For comparative reasons, a clear definition and alignment of measurement tools is a research area for future studies, as existing studies use different methods to define and measure digital readiness, limiting direct comparisons [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Additionally, research should employ longitudinal designs to understand how digital readiness evolves and what influences it, as well as identify effective enhancement strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study revealed moderate digital readiness among individuals with hip or knee OA and low agreement on digital solution efficiency and utilization. We identified three digital readiness profiles distinguished by age, sex, education level, and number of comorbidities. This underscores the importance of considering digital readiness levels when designing and implementing digital health interventions to reduce digital disparities. Our results demonstrate the necessity of taking additional measures when introducing digital health solutions, emphasizing that digital options should be one of several health intervention strategies. In-person interventions remain essential for rehabilitation or exercise for those who are not ready to use digital health solutions. Therefore, the focus should be on a person-centered approach, stratifying health initiatives to meet diverse needs and allocate healthcare more sustainably.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval for GLA:D\u0026reg; was waived by the North Denmark Region\u0026apos;s ethics committee. The GLA:D\u0026reg; registry is approved by the Danish Data Protection Agency (SDU; 10.084, 11.847). Under the Danish Data Protection Act, patient consent was not needed, as data were used for research and statistical purposes. This study adheres to the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available for research purposes upon reasonable request. Contact Ewa M. Roos [email protected] or S\u0026oslash;ren T. Skou [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGZ, LHT, LT, DGT, EMR, and STS designed and conceptualized the study. GZ, STS, EMR, and DGT contributed to the acquisition of data. GZ conducted the analysis and drafted the manuscript. STS provided senior supervision by interpreting findings and critically revising the manuscript. LHT, LT, DGT, and EM contributed with substantive manuscript revisions. All authors reviewed and approved the final manuscript, agreed to be accountable for their own contributions, and ensured the integrity and accuracy of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEMR is the copyright holder of the KOOS and several other patient-reported outcome measures and cofounders of GLA:D\u0026reg;, a not-for-profit initiative to implement clinical guidelines in primary care hosted by the University of Southern Denmark. STS has received personal fees from Munksgaard, TrustMe-Ed, and Nestl\u0026eacute; Health Science outside the submitted work and is cofounder of GLA:D\u0026reg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initiation of GLA:D\u003csup\u003e\u0026reg;\u003c/sup\u003e was partly funded by the Danish Physiotherapy Association\u0026apos;s fund for research, education, and practice development; the Danish Rheumatism Association; and the Physiotherapy Practice Foundation. This study received funding from Region Zealand (Exercise First), the NSR Research Fund (no. A447), and a one-year PhD faculty scholarship from the University of Southern Denmark. The funders had no role in the study design, data collection, analysis, interpretation, report writing, or submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the clinicians and participants for contributing data to the GLA:D\u003csup\u003e\u0026reg;\u0026nbsp;\u003c/sup\u003edatabase. Furthermore, we also want to thank Dr. Arjun Bhalla for providing the original eHealth Readiness Questionnaire and the permission to translate it. Additionally, we thank Mette Dideriksen and Vicky Joshi for the questionnaire translation, and the patient partners Margit Dybkj\u0026aelig;r and Gregers Aagaard for their valuable feedback on the questionnaire. We acknowledge the use of large language models in this manuscript, which are limited to minimal tasks such as spelling and grammar checks.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSteinmetz JD, Culbreth GT, Haile LM, Rafferty Q, Lo J, Fukutaki KG, et al. Global, regional, and national burden of osteoarthritis, 1990\u0026ndash;2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5:e508\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eShah N, Costello K, Mehta A, Kumar D. Applications of Digital Health Technologies in Knee Osteoarthritis: Narrative Review. JMIR Rehabil Assist Technol. 2022;9:e33489.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on digital health 2020-2025 [Internet]. Geneva: World Health Organization; 2021 [cited 2022 Mar 17]. 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Health Soc Care Community. 2020;28:1827\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eRising KL, Guth A, Gentsch AT, Martin Gonzalez K, Hass R, Shughart L, et al. Development and Preliminary Validation of a Screener for Digital Health Readiness. JAMA Netw Open. 2024;7:e2432718.\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet Lond Engl. 2007;370:1453\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSkou ST, Roos EM. Good Life with osteoArthritis in Denmark (GLA:D\u003csup\u003eTM\u003c/sup\u003e): evidence-based education and supervised neuromuscular exercise delivered by certified physiotherapists nationwide. BMC Musculoskelet Disord. 2017;18:72.\u003c/li\u003e\n\u003cli\u003eSakellariou G, Conaghan PG, Zhang W, Bijlsma JWJ, Boyesen P, D\u0026rsquo;Agostino MA, et al. 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Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF. Arthritis Care Res. 2011;63:S240\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eDanquah IH, Petersen CB, Skov SS, Tolstrup JS. Validation of the NPAQ-short \u0026ndash; a brief questionnaire to monitor physical activity and compliance with the WHO recommendations. BMC Public Health. 2018;18:601.\u003c/li\u003e\n\u003cli\u003eKu P-W, Steptoe A, Liao Y, Hsueh M-C, Chen L-J. A cut-off of daily sedentary time and all-cause mortality in adults: a meta-regression analysis involving more than 1 million participants. BMC Med. 2018;16:74.\u003c/li\u003e\n\u003cli\u003eGandek B, Roos EM, Franklin PD, Ware JE. A 12-item short form of the Hip disability and Osteoarthritis Outcome Score (HOOS-12): tests of reliability, validity and responsiveness. Osteoarthritis Cartilage. 2019;27:754\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eGandek B, Roos EM, Franklin PD, Ware JE. A 12-item short form of the Knee injury and Osteoarthritis Outcome Score (KOOS-12): tests of reliability, validity and responsiveness. Osteoarthritis Cartilage. 2019;27:762\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eJensen CE, S\u0026oslash;rensen SS, Gudex C, Jensen MB, Pedersen KM, Ehlers LH. The Danish EQ-5D-5L Value Set: A Hybrid Model Using cTTO and DCE Data. Appl Health Econ Health Policy. 2021;19:579\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eDobson F, Hinman RS, Roos EM, Abbott JH, Stratford P, Davis AM, et al. OARSI recommended performance-based tests to assess physical function in people diagnosed with hip or knee osteoarthritis. Osteoarthritis Cartilage. 2013;21:1042\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eMuth\u0026eacute;n LK, Muth\u0026eacute;n BO. Mplus version 8.10 [Internet]. Eighth Edition. Los Angeles, CA: Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n; 1998. Available from: http://www.statmodel.com/html_ug.shtml\u003c/li\u003e\n\u003cli\u003eKongsted A, Nielsen AM. Latent Class Analysis in health research. J Physiother. 2017;63:55\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eNylund KL, Asparouhov T, Muth\u0026eacute;n BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Struct Equ Model Multidiscip J. 2007;14:535\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003eStataCorp. Stata Statistical Software: Release 18. April 2023, College Station, TX: StataCorp LLC.\u003c/li\u003e\n\u003cli\u003eBricca A, Pellegrini A, Zangger G, Ahler J, J\u0026auml;ger M, Skou ST. The Quality of Health Apps and Their Potential to Promote Behavior Change in Patients With a Chronic Condition or Multimorbidity: Systematic Search in App Store and Google Play. JMIR MHealth UHealth. 2022;10:e33168.\u003c/li\u003e\n\u003cli\u003eRossen S, Kayser L, Vibe-Petersen J, Ried-Larsen M, Christensen JF. Technology in exercise-based cancer rehabilitation: a cross-sectional study of receptiveness and readiness for e-Health utilization in Danish cancer rehabilitation. Acta Oncol. 2019;58:610\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBergh S, Benth J\u0026Scaron;, H\u0026oslash;gset LD, Rydjord B, Kayser L. Assessment of Technology Readiness in Norwegian Older Adults With Long-Term Health Conditions Receiving Home Care Services: Cross-Sectional Questionnaire Study. JMIR Aging. 2025;8:e62936.\u003c/li\u003e\n\u003cli\u003eThorsen IK, Rossen S, Gl\u0026uuml;mer C, Midtgaard J, Ried-Larsen M, Kayser L. Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study. J Med Internet Res. 2020;22:e21195. \u003c/li\u003e\n\u003cli\u003eRosenmeier N, Busk D, Dichman C, Nielsen KM, Kayser L, Wagner MK. Technology Readiness Level and Self-Reported Health in Recipients of an Implantable Cardioverter Defibrillator: Cross-Sectional Study. JMIR Cardio. 2025;9:e58219.\u003c/li\u003e\n\u003cli\u003eTreskes RW, Koole M, Kauw D, Winter MM, Monteiro M, Dohmen D, et al. Adults with congenital heart disease: ready for mobile health? Neth Heart J. 2019;27:152\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eElena-Bucea A, Cruz-Jesus F, Oliveira T, Coelho PS. Assessing the Role of Age, Education, Gender and Income on the Digital Divide: Evidence for the European Union. Inf Syst Front. 2021;23:1007\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eGordon NP, Hornbrook MC. Older adults\u0026rsquo; readiness to engage with eHealth patient education and self-care resources: a cross-sectional survey. BMC Health Serv Res. 2018;18.\u003c/li\u003e\n\u003cli\u003ePiirtola M, Kaprio J, Svedberg P, Silventoinen K, Ropponen A. Associations of sitting time with leisure-time physical inactivity, education, and body mass index change. Scand J Med Sci Sports. 2020;30:322\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eHadgraft NT, Lynch BM, Clark BK, Healy GN, Owen N, Dunstan DW. Excessive sitting at work and at home: Correlates of occupational sitting and TV viewing time in working adults. BMC Public Health. 2015;15:899.\u003c/li\u003e\n\u003cli\u003eSkou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primer. 2022;8:1\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eSmith SM, Wallace E, Clyne B, Boland F, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community setting: a systematic review. Syst Rev. 2021;10:271.\u003c/li\u003e\n\u003cli\u003eKebede AS, Ozolins L-L, Holst H, Galvin K. Digital Engagement of Older Adults: Scoping Review. J Med Internet Res. 2022;24:e40192.\u003c/li\u003e\n\u003cli\u003eKnapova L, Klocek A, Elavsky S. The Role of Psychological Factors in Older Adults\u0026rsquo; Readiness to Use eHealth Technology: Cross-Sectional Questionnaire Study. J Med Internet Res. 2020;22:e14670. \u003c/li\u003e\n\u003cli\u003eNelligan RK, Hinman RS, McManus F, Lamb KE, Bennell KL. Moderators of the Effect of a Self-directed Digitally Delivered Exercise Program for People With Knee Osteoarthritis: Exploratory Analysis of a Randomized Controlled Trial. J Med Internet Res. 2021;23:e30768. \u003c/li\u003e\n\u003cli\u003eLawford BJ, Hinman RS, Kasza J, Nelligan R, Keefe F, Rini C, et al. Moderators of Effects of Internet-Delivered Exercise and Pain Coping Skills Training for People With Knee Osteoarthritis: Exploratory Analysis of the IMPACT Randomized Controlled Trial. J Med Internet Res. 2018;20:e10021. \u003c/li\u003e\n\u003cli\u003eHinman RS, Campbell PK, Kimp AJ, Russell T, Foster NE, Kasza J, et al. Telerehabilitation consultations with a physiotherapist for chronic knee pain versus in-person consultations in Australia: the PEAK non-inferiority randomised controlled trial. Lancet Lond Engl. 2024;403:1267\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eLawford BJ, Bennell KL, Kimp A, Campbell PK, Hinman RS. Understanding Negative and Positive Feelings About Telerehabilitation in People With Chronic Knee Pain: A Mixed-Methods Study. J Orthop Sports Phys Ther. 2024;54:594\u0026ndash;607.\u003c/li\u003e\n\u003cli\u003eYusif S, Hafeez-Baig A, Soar J. e-Health readiness assessment factors and measuring tools: A systematic review. Int J Med Inf. 2017;107:56\u0026ndash;64.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Table 1","content":"\u003cp\u003eSupplementary table 1 is not available with this version. \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Digital readiness, Digital health, Laten class analysis, Osteoarthritis, Exercise therapy, Health education","lastPublishedDoi":"10.21203/rs.3.rs-6312226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6312226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDigital health solutions can support exercise and symptom management in hip and knee osteoarthritis (OA), but their uptake may depend on digital readiness, as measurement of motivation, confidence, and capability in using digital solutions. This study assesses digital readiness profiles in individuals with hip and knee OA starting in-person physiotherapist-supervised exercise therapy and education in primary care (GLA:D\u0026reg;) and their associations with sociodemographic, health, and functional characteristics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBaseline GLA:D\u0026reg; registry questionnaire data were analyzed. The eHealth Readiness Scale (7\u0026ndash;42, lowest to highest) measures digital readiness (e.g., capability to use digital health solutions). Latent class analysis identified digital readiness profiles, and multinomial logistic regression was used to assess associations with selected characteristics and the identified profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe included 3,555 participants (mean age 66.7 years, 67% female), with a mean digital readiness score of 25.6 (SD 8.1). Confidence in internet use was reported by 53%, whereas 32% agreed that it improved efficiency, but only 26% agreed to use lifestyle tracking devices. Three digital readiness profiles emerged: low, intermediate, and high. Compared with the high profile, the low profile was associated with older age (odds ratio (OR) 1.96, 95% confidence interval (CI) 1.71 to 2.24)), being female (OR 0.72, 95% CI 0.57 to 0.90), having a lower education level (OR 0.62, 95% CI 0.45 to 0.88), living alone (OR 1.39, 95% CI 1.11 to 1.76), and having more comorbidities (OR 1.10, 95% CI 1.04 to 1.17). The intermediate profile showed similar patterns in relation to the high profile but participants in the intermediate profile were also less likely among obese individuals (0.70, 95% CI 0.54 to 0.91) and those with higher walking speeds (0.70, 95% CI 0.50 to 0.98).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe three identified digital readiness profiles and associated characteristics such as age, sex, education, and comorbidities emphasize the potential of assessing digital readiness to improve uptake and resource allocation when designing and implementing digital health solutions in clinical settings. Future research should focus on digital readiness improvement strategies.\u003c/p\u003e","manuscriptTitle":"Digital readiness among 3,555 individuals with hip or knee osteoarthritis initiating a supervised education and exercise therapy program: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 09:52:08","doi":"10.21203/rs.3.rs-6312226/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":"22bbbcba-6efd-453d-bc19-e1c3a12263c6","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T16:36:03+00:00","versionOfRecord":{"articleIdentity":"rs-6312226","link":"https://doi.org/10.1002/msc.70127","journal":{"identity":"musculoskeletal-care","isVorOnly":true,"title":"Musculoskeletal Care"},"publishedOn":"2025-06-09 00:00:00","publishedOnDateReadable":"June 9th, 2025"},"versionCreatedAt":"2025-04-07 09:52:08","video":"","vorDoi":"10.1002/msc.70127","vorDoiUrl":"https://doi.org/10.1002/msc.70127","workflowStages":[]},"version":"v1","identity":"rs-6312226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6312226","identity":"rs-6312226","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}