Examining the Factor Structure of Objective Health Literacy and Numeracy Scales

Examining the Factor Structure of Objective Health Literacy and Numeracy Scales | 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 Examining the Factor Structure of Objective Health Literacy and Numeracy Scales Chihiro Moriishi, Keisuke Takano, Takeyuki Oba, Naoki Konishi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794541/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Scales for measuring health literacy and numeracy have been broadly classified into performance-based (objective) and self-reported (subjective) scales. Both types of scales have been widely used in research and practice; however, they are not always consistent and may assess different latent constructs. Furthermore, an increasing number of objective measures have been developed and it is unclear how many latent factors should be assumed. Therefore, we aimed to examine the psychometric properties and factor structure of items assessing objective health literacy across multiple scales and to clarify which aspects of objective health literacy would be correlated with subjective measures, as well as health behaviors and lifestyles. Methods: Five objective scales (72 items in total) were administered to Japanese-speaking adults (N = 16,097; 7722 women; mean age = 54.89). The analyzed scales included items assessing the numeracy, comprehension, and application of health information, some of which were contextualized for specific diseases such as diabetes and cancer. Participants’ responses were submitted to exploratory factor analysis, and individual factor scores were calculated to test correlations with subjective health literacy, health behavior, and lifestyle. Results: Exploratory factor analysis identified three factors, which were interpreted as numeracy, comprehension, and synthesis, respectively. All numeracy items loaded onto the same factor, even when contextualized for different diseases. The comprehension factor consisted of items about medical word comprehension, and the synthesis factor was characterized by items assessing the ability to read and understand health-related information and make judgments on it using their own knowledge. The identified factors showed high inter-factor correlations (rs = 0.54–0.67) and small-to-moderate correlations with subjective health literacy (rs = 0.15–0.44). Additionally, each factor indicated small positive correlations with healthy diet and nutrition and less substance use (rs = 0.19–0.26). Conclusions: Our findings suggest that scales of objective health literacy have at least three latent constructs (numeracy, comprehension, and synthesis) and that disease specificity is not psychometrically prominent. Each factor has some overlap with subjective health literacy, but overall, subjective and objective health literacy should be interpreted as independent constructs given the small-to-modest correlations. objective health literacy objective health numeracy health-related behaviors exploratory factor analysis Figures Figure 1 Introduction Health literacy plays a pivotal role in acquiring and maintaining healthy lifestyles, which help individuals prevent diseases and maintain their well-being [ 1 ]. Although the definition of health literacy varies across studies, the core concept refers to the ability of an individual to obtain, process, understand, and use health information and services [ 2 ]. This conceptualization covers health numeracy, namely, applying numerical and quantitative reasoning skills to navigate a healthcare environment, access care, engage in treatment, and make informed health decisions [ 3 ]. Empirical studies have demonstrated that lower health literacy, including lower health numeracy, is associated with lower autonomy and self-control in health behaviors as well as negative health outcomes, such as higher senior mortality, increased emergency and inpatient facility use, lower medication compliance, and lower preventive service utilization [ 3 , 4 ]. Health literacy assessment has long been a research target, and hundreds of measures have been developed and published over the past three decades [ 5 – 8 ]. Health literacy scales can be categorized into performance-based (objective) and self-reported (subjective) measurements. In objective measurement, individuals are assessed using standardized test stimuli to evaluate their underlying traits, skills, and numeracy [ 9 – 12 ]. For example, the Lipkus numeracy scale (henceforth, Lipkus) requires respondents to perform numeracy tests in general (e.g., Imagine that we rolled a fair, six-sided die 1,000 times. Out of 1,000 rolls, how many times do you think the die would come up even (2, 4, or 6)? ) [ 12 ]. Another typical approach is to assess word comprehension of health-related and medical terms [ 13 ]. It is also common to present responders with hypothetical scenarios or visual materials, such as nutrition labels [ 14 , 15 ] or maps of hospitals [ 16 ], to assess their ability to read, interpret, and process relevant information. In contrast, most subjective measures ask respondents to self-report their perceptions and experiences of handling health information, typically using a Likert scale [ 6 ]. The European Health Literacy Survey Questionnaire (HLS-EU-Q47) [ 17 ] is one of the most widely used measures to assess individuals’ perceived abilities to access, understand, appraise, and apply health information (e.g., Finding information on symptoms of illnesses that concern you is… ; respondents indicate very easy – very difficult ) [ 17 ]. Another example is the Subjective Numeracy Scale (SNS), which assesses individual’s beliefs about their skill in performing various mathematical operations (e.g., How good are you at working with fractions? ), and individuals’ preferences regarding the presentation of numerical information (e.g., When reading the newspaper, how helpful do you find tables and graphs that are parts of a story? ) [ 18 ]. The objective and subjective measures appeared to tap into the same latent construct, that is, the ability to process health information. However, Waters et al. [ 19 ] suggested that these two types of measures assess conceptually related but psychometrically distinct constructs and that numeracy should be separated from general health literacy. Begoray and Kwan [ 20 ] found almost null correlations between objective (word recognition and reading comprehension) and subjective (self-reporting of skills to access and communicate health information) assessments. Marks et al. [ 21 ] suggested that objective measures may reflect medication knowledge, whereas subjective measures may not. For the associations with health outcomes and behaviors, a systematic review [ 22 ] concluded that the evidence is mixed. Several studies observed no differences between performance-based and self-reported health literacy for the associations with relevant health outcomes (e.g., diabetes, stroke, and hypertension), whereas others documented objective-subjective discrepancies (e.g., for cancer screening utilization). Hirsh et al.[ 23 ] noticed that the self-reported disease severity of rheumatoid arthritis was associated with subjective health literacy but not with objective health literacy, including the ability to read and pronounce medical terms. These inconsistencies between objective and subjective measures may make it difficult for researchers and practitioners to determine which type (or both) is included in their assessment batteries. Pros and cons have been outlined in the literature. Most subjective measures are easier to administer and cognitively less demanding for responders [ 6 , 7 ] and are suited to assess meta-cognitive, emotional, or motivational aspects of health literacy rather than knowledge and numeracy ability [ 22 , 24 ]. However, self-reported measures are susceptible to individual response styles and cultural norms (e.g., social desirability and other biases owing to health beliefs [ 18 ]), which may question the precision of the assessment of skills for handling health information [ 9 ]. Objective measures are presumably less influenced by these response biases [ 6 , 25 ], although Nguyen et al. [ 9 ] argued that objective measures can feel like exams and that people know that their skills are being evaluated. This may cause shame or stigma, specifically if individuals do not feel comfortable with exams or are not confident in their skills. Moreover, objective measures often include highly contextualized items that may cover a specific but limited range of knowledge and skills. Another challenge when building an assessment battery for health literacy research is that an enormous number of measures have been developed; thus far, there is no clear guidance on which to use and when [ 8 ]. Recently, we conducted an exploratory factor analysis of 219 items across 11 subjective measures (encompassing 45 subscales), indicating that dimension reduction was effective, as the items were well explained by seven latent factors [ 26 ]. In the current study, we aimed to expand these findings to objective health literacy measures; namely, we conducted an exploratory factor analysis on five performance-based measures of health literacy and numeracy (see the Methods section for the selection criteria of the analyzed scales), including general and disease-specific (i.e., chronic pain, cancer, and diabetes) scales. Through the analyses, we explored how many and what factors would emerge. In addition to the number of factors identified, we were also interested in whether disease-specific items would be recognized as independent factors or factors that reflect common skills and performances regardless of target diseases. Simultaneously, the identified factors were tested for their correlations with lifestyle and health status, as well as subjective health literacy and numeracy, to explore the (in)consistencies (or validity) with perceived health literacy and behaviors. Methods Data Data from a larger longitudinal survey on the health behaviors, psychological characteristics, and lifestyles of Japanese-speaking adults (aged > 18 years living in Japan) were used. The overarching project (still ongoing) includes multiple waves with different focuses: Wave 1 (N = 20,573; early 2023) for physical activity (PA) and psychological characteristics [ 27 ] and for mobile-health technology use [ 28 ], Wave 2 (conducted in 2023; 6 months after Wave 1) for changes in PA and digital health behaviors [ 29 ], and Wave 3 (conducted in early 2024; N = 16,097; 7722 women; mean age = 54.89, standard deviation = 16.46[1] ) for health literacy and lifestyle. Wave 3 included both subjective and objective health literacy scales; the psychometric properties of the subjective scales have been reported elsewhere [ 26 ]. The current study focused exclusively on objective scales. We used data from five objective health literacy (or numeracy) scales together with the validation measures of subjective health literacy, health behavior, and lifestyle (refer to the Measures section). Participants were paid for online panels recruited by a survey firm. All the participants provided informed consent for each wave, and the study was approved by the Ethics Committee of the National Institute of Advanced Industrial Science and Technology (Approval ID: 2022 − 1279). Measures Objective health literacy and numeracy scales We selected the scales for inclusion in the current study following published reviews (e.g., [ 8 , 30 , 31 ], including Tavousi et al. [ 8 ], the latest review on health literacy scales over the past three decades when the study was conceptualized, and Nakadai et al. [ 31 ], a narrative review of the scales available in Japanese). Among the scales listed, we included those that met the following criteria: the scale was available in English or Japanese, could be implemented on a static online survey (i.e., does not require audiovisual materials or in-person interactions), and specific instructions and items were available from published articles, supporting materials, or personal correspondence with the authors of the scales. This selection process resulted in four objective health literacy or numeracy scales: the Lipkus [ 12 , 32 ], newest vital sign scale (NVS) [ 14 , 15 ], functional health literacy scale for young adults (funHLS) [ 13 ], and cancer health literacy scale (CHLT) [ 16 ]. An additional database search (Google Scholar and PubMed) identified the diabetes health numeracy scale (DHN) [ 30 ], which was eligible for the current study. Table 1 summarizes the characteristics of each included scale; most objective health literacy scales are not Likert-type. For example, the funHLS presented medical stem terms (e.g., caries ) and asked participants to indicate the most relevant words for each stem term among three response options (e.g., virus, bacteria, fungus ). Across the scales, each response was binary coded to represent 1 = correct and 0 = incorrect, and the total score was calculated for each scale, with higher values indicating higher levels of objective health literacy or numeracy. Table 1 Overview of the objective health literacy and numeracy scales Scale name (abbreviation) N of items (Cronbach’s alpha) Test format Description 1. Lipkus numeracy scale (Lipkus) [ 12 , 32 ] 10 (0.79) Numeric response questions Measures the ability to understand and use numeric information, particularly for probability: e.g., Imagine that we role a fair, six-sided die 1,000 times. Out of 1,000 roles, how many times do you think the die would come up even (2, 4, or 6)? 2. Newest Vital sign scale (NVS) [ 14 , 15 ] 6 (0.63) Numeric response questions; open-ended questions Measures comprehension, numeracy, and application and evaluation skills. Responders are presented with a nutrition label of ice cream, from which they are required to extract necessary information for calculation (e.g., If you eat the entire container, how many calories will you eat? ) and evaluation (e.g., Pretend that you are allergic to the following substances: penicillin, peanuts, latex gloves, and bee stings. Is it safe for you to eat this ice cream? ) 3. Functional health literacy scale for young adults (funHLS) [ 13 ] 19 (0.93) Multiple choice questions Measures knowledge and comprehension of health-related and medical terms. Responders are presented with stem words, for each of which they are asked to indicate the most relevant among three response options (e.g., Stem = Caries: Response options = Virus, Bacteria, and Fungus). 4. Diabetes health numeracy scale (DHN) [ 26 ] 7 (0.85) Multiple choice questions Measures numeracy skills, contextualized for diabetes (e.g., If you walk for about 30 minutes you can burn 100 calories. If you want to burn 150 calories, how long do you have to walk? ). Several items tap into interpretation skills (e.g., read a table about diagnostic criteria for diabetes, and indicate the stage of an example patient). 5. Cancer health literacy Scale (CHLT) [ 27 ] 30 (0.85) Multiple choice questions Measures knowledge (e.g., Which is the highest in calories and protein? – French fries, cheeseburger, hard-boiled egg ), comprehension skills (e.g., In people who develop oral cancers, 25% of these cases occur in the tongue. Oral cancer occurs in the tongue… ), and their synthesis, contextualized for cancer. [Insert Table 1 about here] Subjective health literacy scale The 47-item HLS-EU-Q47 [ 17 , 33 ] was used to assess subjective health literacy. The HLS-EU-Q47 and other self-report scales (see below) were used as validation measures to test for correlations with objective health literacy measures). The HLS-EU-Q47 measures four information-processing competencies (i.e., how easy it is to access, understand, appraise, and apply health information) for three health-relevant domains (i.e., health care, disease prevention, and health promotion). Participants indicated how applicable each item was to them using a 4-point scale (1 = very easy ; 4 = very difficult ). For ease of interpretation, each item was reverse scored, with higher values indicating higher health literacy levels, and the total score was normalized to a range between 0 and 50 using the following formula: (MEAN-1) × (50/3). This scale has shown good reliability in the current data (Cronbach’s alpha = 0.97). Subjective health numeracy scale The subjective numeracy scale (SNS) was used to assess subjective health numeracy levels [ 18 ]. An SNS measures one’s perceived ability to perform mathematical tasks (e.g., How good are you at working with fractions? ) and preferences for the use of numerical (vs. prose) information (e.g., When reading the newspaper, how helpful do you find tables and graphs that are parts of a story? ). Participants indicated how applicable each item was to them using a 4-point scale (1 = not good at all, not helpful at all ; 4 = very good, very helpful ). This scale has shown good reliability in the current data (Cronbach’s alpha = 0.75). Physical activity The International Physical Activity Questionnaire Short Form (IPAQ-SF; [ 34 , 35 ]) was used to assesses PA levels. Respondents were asked to indicate the number of days and minutes per day spent walking, engaging in moderate-intensity activities, and engaging in vigorous-intensity activities. We did not use sedentary time for the current analyses. The weighted sum of the reported durations was calculated across the three activity categories, representing the total PA in the form of metabolic equivalents (METs-h/w). According to the Ministry of Health, Labour and Welfare in Japan, the recommended amount is 23 METs-h/w or higher for adults aged < 65 years and 10 METs-h/w for older people [ 36 ]. Quality of life and health state Quality of life (QoL) and health status were assessed using the 5-level EQ-5D version [ 37 ]. Participants indicated their health status by selecting the most appropriate statement (i.e., no problems – extreme problems ) for the following five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Participants’ responses were combined into a 5-digit code, which was then converted into a numerical QoL score. The QoL score ranges from − 0.025 to 1, where a negative value signifies a condition worse than death, 0 represents a state equivalent to death, and 1 denotes the highest possible health utility. At the end of the EQ-5D questions, participants were asked to rate their health status using a visual analog scale ranging from 0 to 100, with 0 representing the worst health condition they could imagine and 100 representing the best health condition they could imagine. Health-related lifestyles The Short Multidimensional Inventory Lifestyle Evaluation (SMILE: [ 27 ]) consists of 45 items covering seven domains of health-related lifestyles: diet and nutrition, substance use, PA, strategies to deal with stress, sleep pattern, social support, and environmental exposure. Items asking about the use of illegal drugs (i.e., Items 10 and 11) were excluded to adhere to the ethics standards of the administering survey firm, and the remaining 43 items were used in the survey. Participants rated each item on a 4-point scale (1 = always ; 4 = not at all ). Summed scores were calculated for each domain, whereas items were reverse-scored (with higher values indicating healthier lifestyles). The global score (sum of the seven domains) demonstrated good internal consistency in the current data (Cronbach’s alpha = 0.88). Statistical analyses An exploratory factor analysis was conducted on 72 items across the five objective health literacy and numeracy scales. As each item was binary scored (correct vs. incorrect), polychoric correlations were calculated and used in factor analysis. The number of factors was determined based on the reduction in eigenvalues (i.e., a scree plot), as well as on the interpretability of the identified factors. For each factor, items with factor loadings of 0.40 or greater were interpreted and were used to calculate a factor score (as the mean of raw item scores). These factor scores were tested for correlations with validation measures (i.e., subjective health literacy and numeracy scales, PA, quality of life, health status, and health-related lifestyles). All analyses were performed using R (version 4.3.3; R Foundation for Statistical Computing) and an exploratory factor analysis was performed using the factanal function. Results Descriptive information Table 2 presents the descriptive statistics. For the objective measures, the mean scores were comparable to those reported in previous studies—for example, on the general Japanese population (Lipkus, mean = 9.6) [ 32 ], an Italian population-based sample (NVS, mean = 4.1) [ 38 ], and a sample from the USA (CHLT, M = 22.3) [ 39 ]. The total score on the HLS-EU-Q47 was slightly higher than that reported among Japanese people (M = 25.3) but lower than that reported among Europeans in the literature (M = 33.8) [ 33 ]. Table 2 Descriptive statistics (N = 16097) Variable Mean (SD), n (%) Age 54.89 (16.46) Gender (women) 7,722 (48%) Objective health literacy and numeracy scales Lipkus 7.80 (2.34) NVS 3.50 (1.69) funHLS 14.14 (5.21) DHN 5.38 (2.09) CHLT 24.67 (4.89) Subjective health literacy and numeracy scales HLS-EU-Q47 28.23 (8.07) SNS 3.24 (0.67) Abbreviations: SD, Standard deviation; BMI, Body mass index; Lipkus, Lipkus numeracy scale; NVS, Newest vital sign scale; funHLS, Functional health literacy scale for young adults; DHN, Diabetes health numeracy scale; CHLT, Cancer health literacy scale; HLS-EU-Q47, 47-item European Health Literacy Survey Questionnaire; SNS, Subjective numeracy scale. [Insert Table 2 about here] Exploratory factor analysis The factor analysis performed on 72 items across 5 scales revealed eigenvalues of 18.04, 3.27, 2.13, and 1.73 for the 1–4 factor solutions. The reduction in the eigenvalue supported the 3-factor solution, with explained variances of 0.18, 0.16, and 0.09 for the three factors (total explained variance: 0.43). Additionally, the 3-factor solution had good interpretability; the factor loadings are visualized in Fig. 1 , which confirms that no items had double or triple loadings. The exact factor loadings for each item are listed in Table S1 . Table 3 summarizes the characteristics of each factor. Factor 1 (FA1) included items from four out of the five analyzed scales (i.e., Lipkus, NVS, DHN, and CHLT), representing performance-based health numeracy in general (e.g., Imagine that we role a fair, six-sided die 1,000 times. Out of 1,000 roles, how many times do you think the die would come up even (2, 4, or 6)? ). These four scales target different populations—Lipkus was designed for the general population, whereas the other three were contextualized for particular diseases and health conditions (DHN for diabetes and CHLT for cancer). The test format also differed across the four scales; the CHLT used multiple-choice questions, whereas the NVS and DHN included numeric response questions. These results suggest that the items assessing performance-based numeracy correlate well with each other, regardless of heterogeneity in the target diseases and test format. Nevertheless, FA1 included several items representing disease-specific knowledge (e.g., Item 21 of the CHLT: A tumor is considered “inoperable” when it cannot be treated with… ) but not numeracy per se. These non-numeracy items typically had factor loadings on the border (0.40–0.50), which may be excluded when calculating the factor score for conceptual homogeneity. Table 3 Interpretations of identified factors Factor Item with a factor loading of 0.40 or higher Example item FA1 (Numeracy) Lipkus (Items 1–10) NVS (Items 3 and 4) DHN (Items 1–7) CHLT (Items 5, 6, 9, 10. 13, 14, 17, 21, and 23) Lipkus #6: If Person A’s risk of getting a disease is 1% in ten years, and person B’s risk is double that of A’s, what is B’s risk? NVS #3: Your doctor advises you to reduce the amount of saturated fat in your diet. You usually have 42 g of saturated fat each day, which includes 1 serving of ice cream. If you stop eating ice cream, how many grams of saturated fat would you be consuming each day? DHN #2: A male diabetic patient weighs 80 kilograms (kg). The doctor advised this patient to lose 10% of his weight. How much weight does this patient need to lose? CHLT #5: In people who develop oral cancers, 25% of these cases occur in the tongue. Oral cancer occurs in the tongue… FA2 (Comprehension) funHLS (Items 1–14 and 16–19) funHLS #6: Indicate the most relevant word for Vitamin C. Response options: Vegetables, Fat, Grain , and I Don’t know. FA3 (Synthesis) NVS (Items 5, 6) CHLT (Items 2–4, 7–8, 11, 15–16, 18–20, 22, 24–25, 27, 29–30) NVS #5: Pretend that you are allergic to the following substances: Penicillin, peanuts, latex gloves, and bee stings. Is it safe for you to eat this ice cream? CHLT #27: If patients get better by taking Medicine B twice a day, then if they take Medicine B 3 times a day, patients will get better faster. (True or false) Abbreviations: Lipkus, Lipkus scale; NVS, Newest vital sign scale; DHN, Diabetes health numeracy scale; CHLT, Cancer health literacy scale; funHLS, Functional health literacy scale for young adults. The means and standard deviations of each item as well as their factor loadings are provided in the supplementary materials. [Insert Fig. 1 about here] [Insert Table 3 about here] Factor 2 (FA2) consisted exclusively of items from the funHLS, which asked participants to indicate the word most relevant to a stem (medical) word. Items from the funHLS assess word comprehension and knowledge about the diseases and symptoms that young adults often experience, nutrition and diet and biology of the human body. All items of the funHLS, except for Item 15 (Uterocervical cancer), showed loadings of > 0.40 on FA2. Although the funHLS items covered a range of topics (e.g., caries, depression, and body mass index), most items loaded on the same factor, and items from other scales were not included in FA2. This factor could be interpreted as a comprehension of health-related and medical terms in general (i.e., not limited to a particular disease or health condition); however, it is still possible that the factor may reflect the unique test format, as the other scales require binary (true-false) responses or numeric responses, for example, to calculate a probability or health risk. Factor 3 (FA3) included items from two scales, the NVS and CHLT, which assess the ability to process and synthesize health-related information. For example, Item 5 of the NVS concerns abstract reasoning, integrating reading, comprehending, and interpreting skills as applied to material with health content [ 15 ]. Respondents were presented with a hypothetical nutrition label of ice cream and asked to judge whether the ice cream would be safe if the respondents were allergic to the indicated substances. Similarly, many of the items loaded onto FA3 required respondents to comprehend and synthesize the presented information (e.g., the nutrition label) to make the correct response. Items from the CHLT are contextualized in a daily cancer-patient routine at a clinic (e.g., instructions for the use of medicines, reading a floor map of a hospital, etc.), assessing respondents’ knowledge, numeracy, navigation, and synthesis [ 16 ]. Therefore, compared to FA1 (numeracy) and FA2 (word comprehension and knowledge), FA3 is distinguished in that it broadly measures higher-order skills that require the synthesis of multiple skills (e.g., reading, comprehension, and interpretation) to apply in a daily health context. Correlation analysis The three identified factors were tested for their correlations with subjective health literacy and numeracy, as well as with health status and lifestyle (Table 4 ). Each correlation was interpreted for magnitude but not for statistical significance given the large sample size of the analyzed dataset. The Cohen’s guideline was used, with r = 0.10, 0.30, and 0.50 being interpreted as small, moderate, and large effects, respectively [ 40 , 41 ]. FA1–FA3 showed large inter-factor correlations. However, these factors showed small-to-moderate correlations with the HLS-EU-Q47 (subjective health literacy), SNS (subjective numeracy), and SMILE (subscales of diet, nutrition, and substance use). Moreover, FA1 and FA2 showed small correlations with SMILE sleep and social support (rs = 0.11–0.13). None of the factors showed interpretable size correlations with the IPAQ-SF (total PA) or EQ-5D (QoL and subjective health) scores. The two subjective measures, the HLS-EU-Q47 and SNS, presented stronger correlations with the SMILE subscales, except for substance use, than FA1–FA3. Table 4 Correlations between each factor and comprehensive health status Mean (SD) FA1 FA2 FA3 HLS-EU SNS FA1 0.79 (0.21) FA2 0.76 (0.28) 0.67 FA3 0.82 (0.18) 0.67 0.54 HLS-EU-Q47 28.23 (8.07) 0.20 0.24 0.15 SNS 3.24 (0.67) 0.44 0.32 0.32 Total physical activity (METs-h/w) 34.20 (55.21) −0.01 0.00 −0.05 0.09 0.06 EQ-5D Quality of life 0.82 (0.14) 0.03 −0.01 0.02 0.10 0.07 EQ-5D Health status 76.17 (17.59) 0.06 0.05 0.03 0.17 0.12 SMILE Diet 2.88 (0.49) 0.26 0.26 0.20 0.33 0.27 SMILE Substance use 3.29 (0.82) 0.19 0.21 0.21 0.08 0.07 SMILE Physical activity 2.29 (0.62) 0.05 0.05 −0.02 0.24 0.19 SMILE Stress management 2.40 (0.48) 0.09 0.10 0.02 0.32 0.21 SMILE Sleep 2.77 (0.58) 0.11 0.11 0.06 0.23 0.17 SMILE Social support 2.59 (0.63) 0.12 0.13 0.06 0.31 0.23 SMILE Environment 2.44 (0.53) 0.02 0.03 −0.02 0.16 0.13 Abbreviations: SD, Standard deviation; FA1, Factor 1 (numeracy); FA2, Factor 2 (comprehension); FA3, Factor 3 (synthesis); HLS-EU-Q47, 47-item European Health Literacy Survey Questionnaire; SNS, Subjective numeracy scale; SMILE, Short Multidimensional Inventory Lifestyle Evaluation. [Insert Table 4 about here] Discussion This study examined the factor structure of the multi-objective health literacy and numeracy scales among Japanese-speaking adults. Specifically, we explored how many factors would emerge in the pool of 72 items extracted from five scales, with or without being contextualized for specific diseases. The exploratory factor analysis indicated that the items could be categorized into three factors: performance-based numeracy (FA1), comprehension (FA2), and synthesis (FA3). FA1 consisted of items from four scales targeting people with different health conditions and diseases that typically assess their ability to understand numeric information and perform mathematical calculations. Most funHLS items loaded on FA2, assessing the comprehension of health-related and medical terms. The NVS and CHLT items not included in FA1 were identified as FA3, which required the synthesis of multiple skills to handle health information, such as reading, knowledge, navigation, and interpretation skills, to provide a correct response. A correlation analysis indicated that all factors had weak correlations with subjective health literacy, moderate correlations with subjective health numeracy, and weak correlations with lifestyle (diet, nutrition, and substance use). Lifestyles concerning sleep and social support demonstrated small correlations only with FA1 and FA2 but not with FA3. In line with Altin et al. [ 9 ] and Wu et al. [ 41 ], we observed small-to-moderate correlations between the three factors and the subjective scales (i.e. HLS-EU-Q47, SNS). Furthermore, the three identified factors were highly correlated with each other, yet being recognized as independent factors. These findings echo Waters et al.’s [ 15 ] argument—health literacy and numeracy are related but distinct constructs, each of which can be psychometrically divided into performance-based (objective) and self-reported (subjective) constructs. Another important point is that our analysis did not identify disease-specific factors, although we included cancer- and diabetes-specific items in the item pool. Therefore, it is plausible to assume that the three identified factors—numeracy, comprehension, and synthesis—form a common basis for processing health information in general. However, notably, the distinctions between the three factors are not always clear, as some items loaded on FA1 tapped into knowledge and interpretation but not numeracy, and FA3 (synthesis) appeared to require the integration of different skills that are reflected in FA1 and FA2. Thus, we could not draw a solid conclusion about how many skills should be assumed and what hierarchy or dependency of those skills could best explain health literacy, which might be an essential direction for future research to establish a taxonomy of objectively measured (or measurable) health literacy skills, both at conceptual and psychometric levels. Regarding the associations with health behaviors and lifestyles, each factor presented small correlations with diet and substance use but not with PA. Some overlaps were noticed at the item-content level; for example, the NVS includes items about caloric calculation as well as reading and interpreting a nutrition label, whereas the SMILE asks how often respondents eat high-calorie sweet or fatty foods and how frequently they check the food ingredient labels. A similar association was found in patients with diabetes; performance-based numeracy is positively correlated with a healthy diet [ 42 ]. These findings suggest that skills and abilities assessed by objective measures underlie perceived health behaviors (e.g., individuals are able to read and interpret ingredient labels and check them regularly when shopping for food). Compared with objective measures, subjective measures demonstrated overall larger correlations with health behaviors and lifestyles. The numeracy and comprehension factors (FA1 and FA2) had small correlations with sleep and social support of the SMILE (rs = 0.11–0.13) but subjective health literacy (HLS-EU-Q47) and numeracy (SNS) presented slightly larger correlations with sleep and nutrition (rs = 0.17–0.33) as well as with other subscales (e.g., PA, r = 0.24; stress management, r = 0.33). Higher levels of objective health literacy are thought to be associated with an inclination to behave in a manner that is beneficial to one’s own and others’ health (e.g., choosing beneficial treatments for a disease) [ 21 ]. However, subjective health literacy may share even larger variance with the perception of health behaviors; that is, how people perceive their ability to process health information may overlap with how they believe to behave in a context where their health matters. It is too early to conclude that subjective measures are more suited for studying health behaviors based only on the correlations found in the current study. Instead, it is fair to argue that objective and subjective measures reflect different psychological processes, and further research is warranted to clarify which type (or both) of health literacy measure is associated with actual health behaviors that can be assessed using sensors and devices, such as accelerometers for PA. This study has several methodological limitations. First, the item pool was neither exhaustive nor comprehensive. Importantly, we did not include TOFHLA [ 10 ] and REALM [ 11 ], which are the most widely used objective measures, because of language and cultural differences (all materials had to be in the Japanese language) and technical limitations of the survey platform (audio-visual recording could not be implemented). It is highly likely that the results of the factor analysis and subsequent analyses would differ if the item pool were expanded. Second, diagnostic information on physical or mental disorders was not collected. Testing patients with a particular disease or disorder was out of our focus, as we set a community sample as our target population. Health literacy is essential in maintaining one’s health and preventing future diseases. However, it is important to widen the focus to include patient care and disease management, for which health literacy and assessments are highly relevant. Third, convenient self-reporting tools were used to assess PA and lifestyle habits. Health behaviors can be assessed using wearable devices and e-diaries (e.g., food recordings), which may allow for a more reliable estimation of healthy lifestyles [ 42 ]. It was technically impossible for us to use device- or sensor-based assessments given the sample size of the current study, but objective assessment tools could be considered when a focused sample is the research target. Conclusions Despite these limitations, our findings contribute to the psychometric evidence base of objective health literacy and numeracy scales. The results of the exploratory factor analysis identified three factors—numeracy, comprehension, and synthesis—among 72 items from five scales, independent of disease specificity and different contextualizations of the items. These three factors showed marginal correlations with subjective measures of health literacy and numeracy, highlighting the distinction between performance-based and self-reported assessment approaches [ 43 ]. Researchers and practitioners should be aware that self-report measures do not always reflect the skills and abilities reflected in performance on tests assessing numeracy, comprehension, and more integrated information processing skills. However, we do not have clear guidance on when to use objective or subjective measures, as our results revealed no prominent correlation with a particular health behavior. If one wants to assess performance-based health literacy and numeracy, administering generic items reflecting the three factors may suffice; additionally, contextualizing the items to specific diseases may be psychometrically less important. Future research could find a way to optimize the item set for practical use, which would reduce the time of administration and burden on responders. Abbreviations CHLT: Cancer health literacy scale DHN: Diabetes health numeracy scale funHLS: Functional health literacy scale for young adults HLS-EU-Q47: 47-item European Health Literacy Survey Questionnaire IPAQ-SF: International Physical Activity Questionnaire Short Form Lipkus: Lipkus numeracy scale NVS: Newest vital sign scale PA: Physical activity SD: Standard deviation SMILE: Short Multidimensional Inventory Lifestyle Evaluation SNS: Subjective numeracy scale QoL: Quality of life VAS: visual analog scale Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the National Institute of Advanced Industrial Science and Technology (Approval ID: 2022-1279). Informed consent was obtained from all the participants. Consent for publication Not applicable. Availability of data and materials The datasets used or analyzed in the study are available from the corresponding author upon reasonable request. Competing interests The authors declare no conflicts of interest concerning the study. The authors are independent of the Fitbit products and their developers. Funding This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), "Innovation of Inclusive Community Platform" Grant Number JPJ012248 (Funding Agency: National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN)). Authors’ contributions Chihiro Moriishi: Methodology, Formal Analysis, Writing – Original draft preparation. Keisuke Takano: Conceptualization, Methodology, Data curation, Supervision, Writing – Reviewing and Editing. Takeyuki Oba: Data curation, Writing – Reviewing and Editing. Naoki Konishi: Methodology, Writing – Reviewing and Editing. Kentaro Katahira: Writing – Reviewing and Editing. Kenta Kimura: Project administration, Writing – Reviewing and Editing Acknowledgements None. Authors’ information (optional) All authors and Affiliations Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan Corresponding author Correspondence to Keisuke Takano. References Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies in the 21st century. Health Promotion International. 2000;15:259–67. Nutbeam D. Health Promotion Glossary. Health Promotion International. 1998;13:349–64. Reyna VF, Nelson WL, Han PK, Dieckmann NF. How numeracy influences risk comprehension and medical decision making. Psychol Bull. 2009;135:943–73. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155:97–107. Haun J, Valerio M, Mccormack L, Sørensen K, Paasche-Orlow M. Health literacy measurement: an inventory and descriptive summary of 51 Instruments. Journal of Health Communication. 2014;19. Nguyen T, Paasche-Orlow M, McCormack L. The state of the science of health literacy measurement. Information Services & Use. 2017;37:189–203. O′Neill B, Gonçalves D, Ricci-Cabello I, Ziebland S, Valderas J. An overview of self-administered health literacy instruments. PLOS ONE. 2014;9:e109110. Tavousi M, Mohammadi S, Sadighi J, Zarei F, Kermani RM, Rostami R, et al. Measuring health literacy: a systematic review and bibliometric analysis of instruments from 1993 to 2021. PLOS ONE. 2022;17:e0271524. Altin SV, Finke I, Kautz-Freimuth S, Stock S. The evolution of health literacy assessment tools: a systematic review. BMC Public Health. 2014;14:1207. Parker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults. J Gen Intern Med. 1995;10:537–41. Davis TC, Crouch MA, Long SW, Jackson RH, Bates P, George RB, et al. Rapid assessment of literacy levels of adult primary care patients. Fam Med. 1991;23:433–5. Lipkus IM, Samsa G, Rimer BK. General performance on a numeracy scale among highly educated samples. Med Decis Making. 2001;21:37–44. Tsubakita T, Kawazoe N, Kasano E. A new functional health literacy scale for Japanese young adults based on item response theory. Asia Pac J Public Health. 2017;29:149–58. Kogure T, Sumitani M, Suka M, Ishikawa H, Odajima T, Igarashi A, et al. Validity and reliability of the Japanese version of the Newest Vital Sign: a preliminary study. PLOS ONE. 2014;9:e94582. Weiss BD, Mays MZ, Martz W, Castro KM, DeWalt DA, Pignone MP, et al. Quick assessment of literacy in primary care: the Newest Vital Sign. Ann Fam Med. 2005;3:514–22. Dumenci L, Matsuyama R, Riddle DL, Cartwright LA, Perera RA, Chung H, et al. Measurement of cancer health literacy and identification of patients with limited cancer health literacy. J Health Commun. 2014;19 0 2:205–24. Sørensen K, Pelikan JM, Röthlin F, Ganahl K, Slonska Z, Doyle G, et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health. 2015;25:1053–8. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making. 2007;27:672–80. Waters EA, Biddle C, Kaphingst KA, Schofield E, Kiviniemi MT, Orom H, et al. Examining the interrelations among objective and subjective health literacy and numeracy and their associations with health knowledge. J GEN INTERN MED. 2018;33:1945–53. Begoray DL, Kwan B. A Canadian exploratory study to define a measure of health literacy. Health Promotion International. 2012;27:23–32. Marks JR, Schectman JM, Groninger H, Plews-Ogan ML. The association of health literacy and socio-demographic factors with medication knowledge. Patient Educ Couns. 2010;78:372–6. Kiechle ES, Bailey SC, Hedlund LA, Viera AJ, Sheridan SL. Different measures, different outcomes? A systematic review of performance-based versus self-reported measures of health literacy and numeracy. J GEN INTERN MED. 2015;30:1538–46. Hirsh JM, Boyle DJ, Collier DH, Oxenfeld AJ, Nash A, Quinzanos I, et al. Limited health literacy is a common finding in a public health hospital’s rheumatology clinic and is predictive of disease severity. J Clin Rheumatol. 2011;17:236–41. Peters E, Bjalkebring P. Multiple numeric competencies: when a number is not just a number. Journal of Personality and Social Psychology. 2015;108:802–22. Schulz PJ, Pessina A, Hartung U, Petrocchi S. Effects of objective and subjective health literacy on patients’ accurate judgment of health information and decision-making ability: survey study. Journal of Medical Internet Research. 2021;23:e20457. Moriishi C, Takano K, Oba T, Konishi N, Katahira K, Kimura K. Factor Structure of Self-reported Health Literacy Scales: a Large-scale Cross-sectional Study. Oba T, Takano K, Katahira K, Kimura K. Exploring individual, social, and environmental factors related to physical activity: a network analysis. 2024;:2024.03.22.24304703. Oba T, Takano K, Katahira K, Kimura K. Use patterns of smartphone apps and wearable devices supporting physical activity and exercise: large-scale cross-sectional survey. JMIR mHealth and uHealth. 2023;11:e49148. Konishi N, Oba T, Takano K, Katahira K, Kimura K. What Functions of smartphone apps and wearable devices promote physical activity? Six-month prospective study on Japanese-speaking adults. JMIR Preprints. 2024. Lee E-H, Lee YW, Lee K-W, Hong S, Kim SH. A new objective health numeracy test for patients with Type 2 Diabetes: development and evaluation of psychometric properties. Asian Nursing Research. 2020;14:66–72. Nakadai K, Kasamaki J, Shimofure T. Levels of the preventive medicine and the competence measured by health literacy scale in Japan—the literature review—. Health and Behavior Sciences. 2018;16:73–93. Okamoto M, Kyutoku Y, Sawada M, Clowney L, Watanabe E, Dan I, et al. Health numeracy in Japan: measures of basic numeracy account for framing bias in a highly numerate population. BMC Med Inform Decis Mak. 2012;12:104. Nakayama K, Osaka W, Togari T, Ishikawa H, Yonekura Y, Sekido A, et al. Comprehensive health literacy in Japan is lower than in Europe: a validated Japanese-language assessment of health literacy. BMC Public Health. 2015;15:505. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–95. Murase N, Katsumura T, Ueda C, Inoue S, Shimomitsu T. Validity and reliability of Japanese version of International Physical Activity Questionnaire. Journal of Health and Welfare Statistics. 2002;49:1–9. Ministry of Health, Labor and Welfare. Physical Activity Standards for Health Promotion 2013. Accessed March 15, 2024. https://www.e-healthnet.mhlw.go.jp/information/policy/guidelines_2013.html. Accessed 22 Apr 2024. Shiroiwa T, Ikeda S, Noto S, Igarashi A, Fukuda T, Saito S, et al. Comparison of value set based on DCE and/or TTO data: scoring for EQ-5D-5L health states in Japan. Value in Health. 2016;19:648–54. Bonaccorsi G, Lastrucci V, Vettori V, Lorini C. Functional health literacy in a population-based sample in Florence: a cross-sectional study using the Newest Vital Sign. BMJ Open. 2019;9:e026356. Dumenci L, Matsuyama RK, Riddle DL, Cartwright L, Siminoff LA. Validation of the Cancer Health Literacy Test-30 for populations without cancer. Health Lit Res Pract. 2018;2:e58–66. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd edition. New York: Routledge; 1988. Cohen J. A power primer. Psychological Bulletin. 1992;112:155–9. Rudolf K, Biallas B, Dejonghe LAL, Grieben C, Rückel L-M, Schaller A, et al. Influence of health literacy on the physical activity of working adults: a cross-sectional analysis of the TRISEARCH Trial. International Journal of Environmental Research and Public Health. 2019;16:4948. Marciano L, Camerini A-L, Schulz PJ. The role of health literacy in diabetes knowledge, self-care, and glycemic control: a meta-analysis. J GEN INTERN MED. 2019;34:1007–17. Footnotes See [26] for the detailed demographic characteristics of the sample. Additional Declarations No competing interests reported. Supplementary Files Supplementob.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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maintaining healthy lifestyles, which help individuals prevent diseases and maintain their well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the definition of health literacy varies across studies, the core concept refers to the ability of an individual to obtain, process, understand, and use health information and services [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This conceptualization covers health numeracy, namely, applying numerical and quantitative reasoning skills to navigate a healthcare environment, access care, engage in treatment, and make informed health decisions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Empirical studies have demonstrated that lower health literacy, including lower health numeracy, is associated with lower autonomy and self-control in health behaviors as well as negative health outcomes, such as higher senior mortality, increased emergency and inpatient facility use, lower medication compliance, and lower preventive service utilization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealth literacy assessment has long been a research target, and hundreds of measures have been developed and published over the past three decades [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Health literacy scales can be categorized into performance-based (objective) and self-reported (subjective) measurements. In objective measurement, individuals are assessed using standardized test stimuli to evaluate their underlying traits, skills, and numeracy [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example, the Lipkus numeracy scale (henceforth, Lipkus) requires respondents to perform numeracy tests in general (e.g., \u003cem\u003eImagine that we rolled a fair, six-sided die 1,000 times. Out of 1,000 rolls, how many times do you think the die would come up even (2, 4, or 6)?\u003c/em\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another typical approach is to assess word comprehension of health-related and medical terms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is also common to present responders with hypothetical scenarios or visual materials, such as nutrition labels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] or maps of hospitals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], to assess their ability to read, interpret, and process relevant information.\u003c/p\u003e \u003cp\u003eIn contrast, most subjective measures ask respondents to self-report their perceptions and experiences of handling health information, typically using a Likert scale [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The European Health Literacy Survey Questionnaire (HLS-EU-Q47) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] is one of the most widely used measures to assess individuals\u0026rsquo; perceived abilities to access, understand, appraise, and apply health information (e.g., \u003cem\u003eFinding information on symptoms of illnesses that concern you is\u0026hellip;\u003c/em\u003e; respondents indicate \u003cem\u003every easy \u0026ndash; very difficult\u003c/em\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Another example is the Subjective Numeracy Scale (SNS), which assesses individual\u0026rsquo;s beliefs about their skill in performing various mathematical operations (e.g., \u003cem\u003eHow good are you at working with fractions?\u003c/em\u003e), and individuals\u0026rsquo; preferences regarding the presentation of numerical information (e.g., \u003cem\u003eWhen reading the newspaper, how helpful do you find tables and graphs that are parts of a story?\u003c/em\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective and subjective measures appeared to tap into the same latent construct, that is, the ability to process health information. However, Waters et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] suggested that these two types of measures assess conceptually related but psychometrically distinct constructs and that numeracy should be separated from general health literacy. Begoray and Kwan [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] found almost null correlations between objective (word recognition and reading comprehension) and subjective (self-reporting of skills to access and communicate health information) assessments. Marks et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] suggested that objective measures may reflect medication knowledge, whereas subjective measures may not. For the associations with health outcomes and behaviors, a systematic review [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] concluded that the evidence is mixed. Several studies observed no differences between performance-based and self-reported health literacy for the associations with relevant health outcomes (e.g., diabetes, stroke, and hypertension), whereas others documented objective-subjective discrepancies (e.g., for cancer screening utilization). Hirsh et al.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] noticed that the self-reported disease severity of rheumatoid arthritis was associated with subjective health literacy but not with objective health literacy, including the ability to read and pronounce medical terms.\u003c/p\u003e \u003cp\u003eThese inconsistencies between objective and subjective measures may make it difficult for researchers and practitioners to determine which type (or both) is included in their assessment batteries. Pros and cons have been outlined in the literature. Most subjective measures are easier to administer and cognitively less demanding for responders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and are suited to assess meta-cognitive, emotional, or motivational aspects of health literacy rather than knowledge and numeracy ability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, self-reported measures are susceptible to individual response styles and cultural norms (e.g., social desirability and other biases owing to health beliefs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), which may question the precision of the assessment of skills for handling health information [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Objective measures are presumably less influenced by these response biases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], although Nguyen et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] argued that objective measures can feel like exams and that people know that their skills are being evaluated. This may cause shame or stigma, specifically if individuals do not feel comfortable with exams or are not confident in their skills. Moreover, objective measures often include highly contextualized items that may cover a specific but limited range of knowledge and skills.\u003c/p\u003e \u003cp\u003eAnother challenge when building an assessment battery for health literacy research is that an enormous number of measures have been developed; thus far, there is no clear guidance on which to use and when [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recently, we conducted an exploratory factor analysis of 219 items across 11 subjective measures (encompassing 45 subscales), indicating that dimension reduction was effective, as the items were well explained by seven latent factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the current study, we aimed to expand these findings to objective health literacy measures; namely, we conducted an exploratory factor analysis on five performance-based measures of health literacy and numeracy (see the Methods section for the selection criteria of the analyzed scales), including general and disease-specific (i.e., chronic pain, cancer, and diabetes) scales. Through the analyses, we explored how many and what factors would emerge. In addition to the number of factors identified, we were also interested in whether disease-specific items would be recognized as independent factors or factors that reflect common skills and performances regardless of target diseases. Simultaneously, the identified factors were tested for their correlations with lifestyle and health status, as well as subjective health literacy and numeracy, to explore the (in)consistencies (or validity) with perceived health literacy and behaviors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eData from a larger longitudinal survey on the health behaviors, psychological characteristics, and lifestyles of Japanese-speaking adults (aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years living in Japan) were used. The overarching project (still ongoing) includes multiple waves with different focuses: Wave 1 (N\u0026thinsp;=\u0026thinsp;20,573; early 2023) for physical activity (PA) and psychological characteristics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and for mobile-health technology use [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Wave 2 (conducted in 2023; 6 months after Wave 1) for changes in PA and digital health behaviors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and Wave 3 (conducted in early 2024; N\u0026thinsp;=\u0026thinsp;16,097; 7722 women; mean age\u0026thinsp;=\u0026thinsp;54.89, standard deviation\u0026thinsp;=\u0026thinsp;16.46[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e) for health literacy and lifestyle. Wave 3 included both subjective and objective health literacy scales; the psychometric properties of the subjective scales have been reported elsewhere [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The current study focused exclusively on objective scales. We used data from five objective health literacy (or numeracy) scales together with the validation measures of subjective health literacy, health behavior, and lifestyle (refer to the Measures section). Participants were paid for online panels recruited by a survey firm. All the participants provided informed consent for each wave, and the study was approved by the Ethics Committee of the National Institute of Advanced Industrial Science and Technology (Approval ID: 2022\u0026thinsp;\u0026minus;\u0026thinsp;1279).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eObjective health literacy and numeracy scales\u003c/h2\u003e \u003cp\u003eWe selected the scales for inclusion in the current study following published reviews (e.g., [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], including Tavousi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the latest review on health literacy scales over the past three decades when the study was conceptualized, and Nakadai et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], a narrative review of the scales available in Japanese). Among the scales listed, we included those that met the following criteria: the scale was available in English or Japanese, could be implemented on a static online survey (i.e., does not require audiovisual materials or in-person interactions), and specific instructions and items were available from published articles, supporting materials, or personal correspondence with the authors of the scales. This selection process resulted in four objective health literacy or numeracy scales: the Lipkus [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], newest vital sign scale (NVS) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], functional health literacy scale for young adults (funHLS) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and cancer health literacy scale (CHLT) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An additional database search (Google Scholar and PubMed) identified the diabetes health numeracy scale (DHN) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which was eligible for the current study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the characteristics of each included scale; most objective health literacy scales are not Likert-type. For example, the funHLS presented medical stem terms (e.g., \u003cem\u003ecaries\u003c/em\u003e) and asked participants to indicate the most relevant words for each stem term among three response options (e.g., \u003cem\u003evirus, bacteria, fungus\u003c/em\u003e). Across the scales, each response was binary coded to represent 1\u0026thinsp;=\u0026thinsp;correct and 0\u0026thinsp;=\u0026thinsp;incorrect, and the total score was calculated for each scale, with higher values indicating higher levels of objective health literacy or numeracy.\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\u003eOverview of the objective health literacy and numeracy scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale name (abbreviation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e of items\u003c/p\u003e \u003cp\u003e(Cronbach\u0026rsquo;s alpha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest format\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Lipkus numeracy scale (Lipkus) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumeric response questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures the ability to understand and use numeric information, particularly for probability: e.g., \u003cem\u003eImagine that we role a fair, six-sided die 1,000 times. Out of 1,000 roles, how many times do you think the die would come up even (2, 4, or 6)?\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Newest Vital sign scale (NVS) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumeric response questions; open-ended questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures comprehension, numeracy, and application and evaluation skills. Responders are presented with a nutrition label of ice cream, from which they are required to extract necessary information for calculation (e.g., \u003cem\u003eIf you eat the entire container, how many calories will you eat?\u003c/em\u003e) and evaluation (e.g., \u003cem\u003ePretend that you are allergic to the following substances: penicillin, peanuts,\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003elatex gloves, and bee stings. Is it safe for you to eat this ice cream?\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Functional health literacy scale for young adults (funHLS) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple choice questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures knowledge and comprehension of health-related and medical terms. Responders are presented with stem words, for each of which they are asked to indicate the most relevant among three response options (e.g., Stem\u0026thinsp;=\u0026thinsp;Caries: Response options\u0026thinsp;=\u0026thinsp;Virus, Bacteria, and Fungus).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Diabetes health numeracy scale (DHN) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple choice questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures numeracy skills, contextualized for diabetes (e.g., \u003cem\u003eIf you walk for about 30 minutes you can burn 100 calories. If you want to burn 150 calories, how long do you have to walk?\u003c/em\u003e). Several items tap into interpretation skills (e.g., read a table about diagnostic criteria for diabetes, and indicate the stage of an example patient).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Cancer health literacy Scale (CHLT) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple choice questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures knowledge (e.g., \u003cem\u003eWhich is the highest in calories and protein? \u0026ndash; French fries, cheeseburger, hard-boiled egg\u003c/em\u003e), comprehension skills (e.g., \u003cem\u003eIn people who develop oral cancers, 25% of these cases occur in the tongue. Oral cancer occurs in the tongue\u0026hellip;\u003c/em\u003e), and their synthesis, contextualized for cancer.\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\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjective health literacy scale\u003c/h3\u003e\n\u003cp\u003eThe 47-item HLS-EU-Q47 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was used to assess subjective health literacy. The HLS-EU-Q47 and other self-report scales (see below) were used as validation measures to test for correlations with objective health literacy measures). The HLS-EU-Q47 measures four information-processing competencies (i.e., how easy it is to access, understand, appraise, and apply health information) for three health-relevant domains (i.e., health care, disease prevention, and health promotion). Participants indicated how applicable each item was to them using a 4-point scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003every easy\u003c/em\u003e; 4\u0026thinsp;=\u0026thinsp;\u003cem\u003every difficult\u003c/em\u003e). For ease of interpretation, each item was reverse scored, with higher values indicating higher health literacy levels, and the total score was normalized to a range between 0 and 50 using the following formula: (MEAN-1) \u0026times; (50/3). This scale has shown good reliability in the current data (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.97).\u003c/p\u003e\n\u003ch3\u003eSubjective health numeracy scale\u003c/h3\u003e\n\u003cp\u003eThe subjective numeracy scale (SNS) was used to assess subjective health numeracy levels [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. An SNS measures one\u0026rsquo;s perceived ability to perform mathematical tasks (e.g., \u003cem\u003eHow good are you at working with fractions?\u003c/em\u003e) and preferences for the use of numerical (vs. prose) information (e.g., \u003cem\u003eWhen reading the newspaper, how helpful do you find tables and graphs that are parts of a story?\u003c/em\u003e). Participants indicated how applicable each item was to them using a 4-point scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003enot good at all, not helpful at all\u003c/em\u003e; 4\u0026thinsp;=\u0026thinsp;\u003cem\u003every good, very helpful\u003c/em\u003e). This scale has shown good reliability in the current data (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhysical activity\u003c/h2\u003e \u003cp\u003eThe International Physical Activity Questionnaire Short Form (IPAQ-SF; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]) was used to assesses PA levels. Respondents were asked to indicate the number of days and minutes per day spent walking, engaging in moderate-intensity activities, and engaging in vigorous-intensity activities. We did not use sedentary time for the current analyses. The weighted sum of the reported durations was calculated across the three activity categories, representing the total PA in the form of metabolic equivalents (METs-h/w). According to the Ministry of Health, Labour and Welfare in Japan, the recommended amount is 23 METs-h/w or higher for adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years and 10 METs-h/w for older people [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality of life and health state\u003c/h3\u003e\n\u003cp\u003eQuality of life (QoL) and health status were assessed using the 5-level EQ-5D version [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Participants indicated their health status by selecting the most appropriate statement (i.e., \u003cem\u003eno problems\u003c/em\u003e \u0026ndash; \u003cem\u003eextreme problems\u003c/em\u003e) for the following five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Participants\u0026rsquo; responses were combined into a 5-digit code, which was then converted into a numerical QoL score. The QoL score ranges from \u0026minus;\u0026thinsp;0.025 to 1, where a negative value signifies a condition worse than death, 0 represents a state equivalent to death, and 1 denotes the highest possible health utility. At the end of the EQ-5D questions, participants were asked to rate their health status using a visual analog scale ranging from 0 to 100, with 0 representing the worst health condition they could imagine and 100 representing the best health condition they could imagine.\u003c/p\u003e\n\u003ch3\u003eHealth-related lifestyles\u003c/h3\u003e\n\u003cp\u003eThe Short Multidimensional Inventory Lifestyle Evaluation (SMILE: [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]) consists of 45 items covering seven domains of health-related lifestyles: diet and nutrition, substance use, PA, strategies to deal with stress, sleep pattern, social support, and environmental exposure. Items asking about the use of illegal drugs (i.e., Items 10 and 11) were excluded to adhere to the ethics standards of the administering survey firm, and the remaining 43 items were used in the survey. Participants rated each item on a 4-point scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003ealways\u003c/em\u003e; 4\u0026thinsp;=\u0026thinsp;\u003cem\u003enot at all\u003c/em\u003e). Summed scores were calculated for each domain, whereas items were reverse-scored (with higher values indicating healthier lifestyles). The global score (sum of the seven domains) demonstrated good internal consistency in the current data (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.88).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAn exploratory factor analysis was conducted on 72 items across the five objective health literacy and numeracy scales. As each item was binary scored (correct vs. incorrect), polychoric correlations were calculated and used in factor analysis. The number of factors was determined based on the reduction in eigenvalues (i.e., a scree plot), as well as on the interpretability of the identified factors. For each factor, items with factor loadings of 0.40 or greater were interpreted and were used to calculate a factor score (as the mean of raw item scores). These factor scores were tested for correlations with validation measures (i.e., subjective health literacy and numeracy scales, PA, quality of life, health status, and health-related lifestyles). All analyses were performed using R (version 4.3.3; R Foundation for Statistical Computing) and an exploratory factor analysis was performed using the \u003cem\u003efactanal\u003c/em\u003e function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive information\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics. For the objective measures, the mean scores were comparable to those reported in previous studies\u0026mdash;for example, on the general Japanese population (Lipkus, mean\u0026thinsp;=\u0026thinsp;9.6) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], an Italian population-based sample (NVS, mean\u0026thinsp;=\u0026thinsp;4.1) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and a sample from the USA (CHLT, M\u0026thinsp;=\u0026thinsp;22.3) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The total score on the HLS-EU-Q47 was slightly higher than that reported among Japanese people (M\u0026thinsp;=\u0026thinsp;25.3) but lower than that reported among Europeans in the literature (M\u0026thinsp;=\u0026thinsp;33.8) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\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\u003eDescriptive statistics (N\u0026thinsp;=\u0026thinsp;16097)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"11\" rowspan=\"12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.89 (16.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (women)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,722 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective health literacy and numeracy scales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipkus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.80 (2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.50 (1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efunHLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.14 (5.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.38 (2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.67 (4.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective health literacy and numeracy scales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLS-EU-Q47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.23 (8.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.24 (0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: SD, Standard deviation; BMI, Body mass index; Lipkus, Lipkus numeracy scale; NVS, Newest vital sign scale; funHLS, Functional health literacy scale for young adults; DHN, Diabetes health numeracy scale; CHLT, Cancer health literacy scale; HLS-EU-Q47, 47-item European Health Literacy Survey Questionnaire; SNS, Subjective numeracy scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExploratory factor analysis\u003c/h2\u003e \u003cp\u003eThe factor analysis performed on 72 items across 5 scales revealed eigenvalues of 18.04, 3.27, 2.13, and 1.73 for the 1\u0026ndash;4 factor solutions. The reduction in the eigenvalue supported the 3-factor solution, with explained variances of 0.18, 0.16, and 0.09 for the three factors (total explained variance: 0.43). Additionally, the 3-factor solution had good interpretability; the factor loadings are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which confirms that no items had double or triple loadings. The exact factor loadings for each item are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the characteristics of each factor. Factor 1 (FA1) included items from four out of the five analyzed scales (i.e., Lipkus, NVS, DHN, and CHLT), representing performance-based health numeracy in general (e.g., \u003cem\u003eImagine that we role a fair, six-sided die 1,000 times. Out of 1,000 roles, how many times do you think the die would come up even (2, 4, or 6)?\u003c/em\u003e). These four scales target different populations\u0026mdash;Lipkus was designed for the general population, whereas the other three were contextualized for particular diseases and health conditions (DHN for diabetes and CHLT for cancer). The test format also differed across the four scales; the CHLT used multiple-choice questions, whereas the NVS and DHN included numeric response questions. These results suggest that the items assessing performance-based numeracy correlate well with each other, regardless of heterogeneity in the target diseases and test format. Nevertheless, FA1 included several items representing disease-specific knowledge (e.g., Item 21 of the CHLT: \u003cem\u003eA tumor is considered \u0026ldquo;inoperable\u0026rdquo; when it cannot be treated with\u0026hellip;\u003c/em\u003e) but not numeracy per se. These non-numeracy items typically had factor loadings on the border (0.40\u0026ndash;0.50), which may be excluded when calculating the factor score for conceptual homogeneity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterpretations of identified factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem with a factor loading of 0.40 or higher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample item\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA1 (Numeracy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipkus (Items 1\u0026ndash;10)\u003c/p\u003e \u003cp\u003eNVS (Items 3 and 4)\u003c/p\u003e \u003cp\u003eDHN (Items 1\u0026ndash;7)\u003c/p\u003e \u003cp\u003eCHLT (Items 5, 6, 9, 10. 13, 14, 17, 21, and 23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLipkus #6: \u003cem\u003eIf Person A\u0026rsquo;s risk of getting a disease is 1% in ten years, and person B\u0026rsquo;s risk is double that of A\u0026rsquo;s, what is B\u0026rsquo;s risk?\u003c/em\u003e\u003c/p\u003e \u003cp\u003eNVS #3: \u003cem\u003eYour doctor advises you to reduce the amount of saturated fat in your diet. You usually have 42 g of saturated fat each day, which includes 1 serving of ice cream. If you stop eating ice cream, how many grams of saturated fat would you be consuming each day?\u003c/em\u003e\u003c/p\u003e \u003cp\u003eDHN #2: \u003cem\u003eA male diabetic patient weighs 80 kilograms (kg). The doctor advised this patient to lose 10% of his weight. How much weight does this patient need to lose?\u003c/em\u003e\u003c/p\u003e \u003cp\u003eCHLT #5: \u003cem\u003eIn people who develop oral cancers, 25% of these cases occur in the tongue. Oral cancer occurs in the tongue\u0026hellip;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA2 (Comprehension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efunHLS (Items 1\u0026ndash;14 and 16\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efunHLS #6: Indicate the most relevant word for \u003cem\u003eVitamin C.\u003c/em\u003e Response options: \u003cem\u003eVegetables, Fat, Grain\u003c/em\u003e, and \u003cem\u003eI Don\u0026rsquo;t know.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA3 (Synthesis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNVS (Items 5, 6)\u003c/p\u003e \u003cp\u003eCHLT (Items 2\u0026ndash;4, 7\u0026ndash;8, 11, 15\u0026ndash;16, 18\u0026ndash;20, 22, 24\u0026ndash;25, 27, 29\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNVS #5: \u003cem\u003ePretend that you are allergic to the following substances: Penicillin, peanuts, latex gloves, and bee stings. Is it safe for you to eat this ice cream?\u003c/em\u003e\u003c/p\u003e \u003cp\u003eCHLT #27: \u003cem\u003eIf patients get better by taking Medicine B twice a day, then if they take Medicine B 3 times a day, patients will get better faster. (True or false)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: Lipkus, Lipkus scale; NVS, Newest vital sign scale; DHN, Diabetes health numeracy scale; CHLT, Cancer health literacy scale; funHLS, Functional health literacy scale for young adults. The means and standard deviations of each item as well as their factor loadings are provided in the supplementary materials.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eFactor 2 (FA2) consisted exclusively of items from the funHLS, which asked participants to indicate the word most relevant to a stem (medical) word. Items from the funHLS assess word comprehension and knowledge about the diseases and symptoms that young adults often experience, nutrition and diet and biology of the human body. All items of the funHLS, except for Item 15 (Uterocervical cancer), showed loadings of \u0026gt;\u0026thinsp;0.40 on FA2. Although the funHLS items covered a range of topics (e.g., caries, depression, and body mass index), most items loaded on the same factor, and items from other scales were not included in FA2. This factor could be interpreted as a comprehension of health-related and medical terms in general (i.e., not limited to a particular disease or health condition); however, it is still possible that the factor may reflect the unique test format, as the other scales require binary (true-false) responses or numeric responses, for example, to calculate a probability or health risk.\u003c/p\u003e \u003cp\u003eFactor 3 (FA3) included items from two scales, the NVS and CHLT, which assess the ability to process and synthesize health-related information. For example, Item 5 of the NVS concerns abstract reasoning, integrating reading, comprehending, and interpreting skills as applied to material with health content [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Respondents were presented with a hypothetical nutrition label of ice cream and asked to judge whether the ice cream would be safe if the respondents were allergic to the indicated substances. Similarly, many of the items loaded onto FA3 required respondents to comprehend and synthesize the presented information (e.g., the nutrition label) to make the correct response. Items from the CHLT are contextualized in a daily cancer-patient routine at a clinic (e.g., instructions for the use of medicines, reading a floor map of a hospital, etc.), assessing respondents\u0026rsquo; knowledge, numeracy, navigation, and synthesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, compared to FA1 (numeracy) and FA2 (word comprehension and knowledge), FA3 is distinguished in that it broadly measures higher-order skills that require the synthesis of multiple skills (e.g., reading, comprehension, and interpretation) to apply in a daily health context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eThe three identified factors were tested for their correlations with subjective health literacy and numeracy, as well as with health status and lifestyle (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Each correlation was interpreted for magnitude but not for statistical significance given the large sample size of the analyzed dataset. The Cohen\u0026rsquo;s guideline was used, with \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, 0.30, and 0.50 being interpreted as small, moderate, and large effects, respectively [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. FA1\u0026ndash;FA3 showed large inter-factor correlations. However, these factors showed small-to-moderate correlations with the HLS-EU-Q47 (subjective health literacy), SNS (subjective numeracy), and SMILE (subscales of diet, nutrition, and substance use). Moreover, FA1 and FA2 showed small correlations with SMILE sleep and social support (rs\u0026thinsp;=\u0026thinsp;0.11\u0026ndash;0.13). None of the factors showed interpretable size correlations with the IPAQ-SF (total PA) or EQ-5D (QoL and subjective health) scores. The two subjective measures, the HLS-EU-Q47 and SNS, presented stronger correlations with the SMILE subscales, except for substance use, than FA1\u0026ndash;FA3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between each factor and comprehensive health status\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFA3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHLS-EU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSNS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.21)\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eFA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eFA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eHLS-EU-Q47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.23 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eSNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.24 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eTotal physical activity (METs-h/w)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.20 (55.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D Quality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D Health status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.17 (17.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.88 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Substance use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.29 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Stress management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.40 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.59 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMILE Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.44 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: SD, Standard deviation; FA1, Factor 1 (numeracy); FA2, Factor 2 (comprehension); FA3, Factor 3 (synthesis); HLS-EU-Q47, 47-item European Health Literacy Survey Questionnaire; SNS, Subjective numeracy scale; SMILE, Short Multidimensional Inventory Lifestyle Evaluation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the factor structure of the multi-objective health literacy and numeracy scales among Japanese-speaking adults. Specifically, we explored how many factors would emerge in the pool of 72 items extracted from five scales, with or without being contextualized for specific diseases. The exploratory factor analysis indicated that the items could be categorized into three factors: performance-based numeracy (FA1), comprehension (FA2), and synthesis (FA3). FA1 consisted of items from four scales targeting people with different health conditions and diseases that typically assess their ability to understand numeric information and perform mathematical calculations. Most funHLS items loaded on FA2, assessing the comprehension of health-related and medical terms. The NVS and CHLT items not included in FA1 were identified as FA3, which required the synthesis of multiple skills to handle health information, such as reading, knowledge, navigation, and interpretation skills, to provide a correct response. A correlation analysis indicated that all factors had weak correlations with subjective health literacy, moderate correlations with subjective health numeracy, and weak correlations with lifestyle (diet, nutrition, and substance use). Lifestyles concerning sleep and social support demonstrated small correlations only with FA1 and FA2 but not with FA3.\u003c/p\u003e \u003cp\u003eIn line with Altin et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Wu et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], we observed small-to-moderate correlations between the three factors and the subjective scales (i.e. HLS-EU-Q47, SNS). Furthermore, the three identified factors were highly correlated with each other, yet being recognized as independent factors. These findings echo Waters et al.\u0026rsquo;s [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] argument\u0026mdash;health literacy and numeracy are related but distinct constructs, each of which can be psychometrically divided into performance-based (objective) and self-reported (subjective) constructs. Another important point is that our analysis did not identify disease-specific factors, although we included cancer- and diabetes-specific items in the item pool. Therefore, it is plausible to assume that the three identified factors\u0026mdash;numeracy, comprehension, and synthesis\u0026mdash;form a common basis for processing health information in general. However, notably, the distinctions between the three factors are not always clear, as some items loaded on FA1 tapped into knowledge and interpretation but not numeracy, and FA3 (synthesis) appeared to require the integration of different skills that are reflected in FA1 and FA2. Thus, we could not draw a solid conclusion about how many skills should be assumed and what hierarchy or dependency of those skills could best explain health literacy, which might be an essential direction for future research to establish a taxonomy of objectively measured (or measurable) health literacy skills, both at conceptual and psychometric levels.\u003c/p\u003e \u003cp\u003eRegarding the associations with health behaviors and lifestyles, each factor presented small correlations with diet and substance use but not with PA. Some overlaps were noticed at the item-content level; for example, the NVS includes items about caloric calculation as well as reading and interpreting a nutrition label, whereas the SMILE asks how often respondents eat high-calorie sweet or fatty foods and how frequently they check the food ingredient labels. A similar association was found in patients with diabetes; performance-based numeracy is positively correlated with a healthy diet [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings suggest that skills and abilities assessed by objective measures underlie perceived health behaviors (e.g., individuals are able to read and interpret ingredient labels and check them regularly when shopping for food).\u003c/p\u003e \u003cp\u003eCompared with objective measures, subjective measures demonstrated overall larger correlations with health behaviors and lifestyles. The numeracy and comprehension factors (FA1 and FA2) had small correlations with sleep and social support of the SMILE (rs\u0026thinsp;=\u0026thinsp;0.11\u0026ndash;0.13) but subjective health literacy (HLS-EU-Q47) and numeracy (SNS) presented slightly larger correlations with sleep and nutrition (rs\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.33) as well as with other subscales (e.g., PA, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24; stress management, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33). Higher levels of objective health literacy are thought to be associated with an inclination to behave in a manner that is beneficial to one\u0026rsquo;s own and others\u0026rsquo; health (e.g., choosing beneficial treatments for a disease) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, subjective health literacy may share even larger variance with the \u003cem\u003eperception\u003c/em\u003e of health behaviors; that is, how people perceive their ability to process health information may overlap with how they believe to behave in a context where their health matters. It is too early to conclude that subjective measures are more suited for studying health behaviors based only on the correlations found in the current study. Instead, it is fair to argue that objective and subjective measures reflect different psychological processes, and further research is warranted to clarify which type (or both) of health literacy measure is associated with \u003cem\u003eactual\u003c/em\u003e health behaviors that can be assessed using sensors and devices, such as accelerometers for PA.\u003c/p\u003e \u003cp\u003eThis study has several methodological limitations. First, the item pool was neither exhaustive nor comprehensive. Importantly, we did not include TOFHLA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and REALM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which are the most widely used objective measures, because of language and cultural differences (all materials had to be in the Japanese language) and technical limitations of the survey platform (audio-visual recording could not be implemented). It is highly likely that the results of the factor analysis and subsequent analyses would differ if the item pool were expanded. Second, diagnostic information on physical or mental disorders was not collected. Testing patients with a particular disease or disorder was out of our focus, as we set a community sample as our target population. Health literacy is essential in maintaining one\u0026rsquo;s health and preventing future diseases. However, it is important to widen the focus to include patient care and disease management, for which health literacy and assessments are highly relevant. Third, convenient self-reporting tools were used to assess PA and lifestyle habits. Health behaviors can be assessed using wearable devices and e-diaries (e.g., food recordings), which may allow for a more reliable estimation of healthy lifestyles [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It was technically impossible for us to use device- or sensor-based assessments given the sample size of the current study, but objective assessment tools could be considered when a focused sample is the research target.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDespite these limitations, our findings contribute to the psychometric evidence base of objective health literacy and numeracy scales. The results of the exploratory factor analysis identified three factors\u0026mdash;numeracy, comprehension, and synthesis\u0026mdash;among 72 items from five scales, independent of disease specificity and different contextualizations of the items. These three factors showed marginal correlations with subjective measures of health literacy and numeracy, highlighting the distinction between performance-based and self-reported assessment approaches [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Researchers and practitioners should be aware that self-report measures do not always reflect the skills and abilities reflected in performance on tests assessing numeracy, comprehension, and more integrated information processing skills. However, we do not have clear guidance on when to use objective or subjective measures, as our results revealed no prominent correlation with a particular health behavior. If one wants to assess performance-based health literacy and numeracy, administering generic items reflecting the three factors may suffice; additionally, contextualizing the items to specific diseases may be psychometrically less important. Future research could find a way to optimize the item set for practical use, which would reduce the time of administration and burden on responders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHLT: Cancer health literacy scale\u003c/p\u003e\n\u003cp\u003eDHN: Diabetes health numeracy scale\u003c/p\u003e\n\u003cp\u003efunHLS: Functional health literacy scale for young adults\u003c/p\u003e\n\u003cp\u003eHLS-EU-Q47: 47-item European Health Literacy Survey Questionnaire\u003c/p\u003e\n\u003cp\u003eIPAQ-SF: International Physical Activity Questionnaire Short Form\u003c/p\u003e\n\u003cp\u003eLipkus: Lipkus numeracy scale\u003c/p\u003e\n\u003cp\u003eNVS: Newest vital sign scale\u003c/p\u003e\n\u003cp\u003ePA: Physical activity\u003c/p\u003e\n\u003cp\u003eSD: Standard deviation\u003c/p\u003e\n\u003cp\u003eSMILE: Short Multidimensional Inventory Lifestyle Evaluation\u003c/p\u003e\n\u003cp\u003eSNS: Subjective numeracy scale\u003c/p\u003e\n\u003cp\u003eQoL: Quality of life\u003c/p\u003e\n\u003cp\u003eVAS: visual analog scale\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the National Institute of Advanced Industrial Science and Technology (Approval ID: 2022-1279). Informed consent was obtained from all the participants.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe datasets used or analyzed in the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest concerning the study. The authors are independent of the Fitbit products and their developers.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), \"Innovation of Inclusive Community Platform\" Grant Number JPJ012248 (Funding Agency: National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN)).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eChihiro Moriishi: Methodology, Formal Analysis, Writing – Original draft preparation. Keisuke Takano: Conceptualization, Methodology, Data curation, Supervision, Writing – Reviewing and Editing. Takeyuki Oba: Data curation, Writing – Reviewing and Editing. Naoki Konishi: Methodology, Writing – Reviewing and Editing. Kentaro Katahira: Writing – Reviewing and Editing. Kenta Kimura: Project administration, Writing – Reviewing and Editing\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAuthors’ information (optional)\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAll authors and Affiliations\u003c/p\u003e\n\u003cp\u003eHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Keisuke Takano.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies in the 21st century. Health Promotion International. 2000;15:259\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eNutbeam D. Health Promotion Glossary. Health Promotion International. 1998;13:349\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eReyna VF, Nelson WL, Han PK, Dieckmann NF. How numeracy influences risk comprehension and medical decision making. Psychol Bull. 2009;135:943\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eBerkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155:97\u0026ndash;107.\u003c/li\u003e\n\u003cli\u003eHaun J, Valerio M, Mccormack L, S\u0026oslash;rensen K, Paasche-Orlow M. Health literacy measurement: an inventory and descriptive summary of 51 Instruments. Journal of Health Communication. 2014;19.\u003c/li\u003e\n\u003cli\u003eNguyen T, Paasche-Orlow M, McCormack L. The state of the science of health literacy measurement. Information Services \u0026amp; Use. 2017;37:189\u0026ndash;203.\u003c/li\u003e\n\u003cli\u003eO\u0026prime;Neill B, Gon\u0026ccedil;alves D, Ricci-Cabello I, Ziebland S, Valderas J. An overview of self-administered health literacy instruments. PLOS ONE. 2014;9:e109110.\u003c/li\u003e\n\u003cli\u003eTavousi M, Mohammadi S, Sadighi J, Zarei F, Kermani RM, Rostami R, et al. Measuring health literacy: a systematic review and bibliometric analysis of instruments from 1993 to 2021. PLOS ONE. 2022;17:e0271524.\u003c/li\u003e\n\u003cli\u003eAltin SV, Finke I, Kautz-Freimuth S, Stock S. The evolution of health literacy assessment tools: a systematic review. BMC Public Health. 2014;14:1207.\u003c/li\u003e\n\u003cli\u003eParker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults. J Gen Intern Med. 1995;10:537\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eDavis TC, Crouch MA, Long SW, Jackson RH, Bates P, George RB, et al. Rapid assessment of literacy levels of adult primary care patients. Fam Med. 1991;23:433\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eLipkus IM, Samsa G, Rimer BK. General performance on a numeracy scale among highly educated samples. Med Decis Making. 2001;21:37\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eTsubakita T, Kawazoe N, Kasano E. A new functional health literacy scale for Japanese young adults based on item response theory. Asia Pac J Public Health. 2017;29:149\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eKogure T, Sumitani M, Suka M, Ishikawa H, Odajima T, Igarashi A, et al. Validity and reliability of the Japanese version of the Newest Vital Sign: a preliminary study. PLOS ONE. 2014;9:e94582.\u003c/li\u003e\n\u003cli\u003eWeiss BD, Mays MZ, Martz W, Castro KM, DeWalt DA, Pignone MP, et al. Quick assessment of literacy in primary care: the Newest Vital Sign. Ann Fam Med. 2005;3:514\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eDumenci L, Matsuyama R, Riddle DL, Cartwright LA, Perera RA, Chung H, et al. Measurement of cancer health literacy and identification of patients with limited cancer health literacy. J Health Commun. 2014;19 0 2:205\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eS\u0026oslash;rensen K, Pelikan JM, R\u0026ouml;thlin F, Ganahl K, Slonska Z, Doyle G, et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health. 2015;25:1053\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eFagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making. 2007;27:672\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eWaters EA, Biddle C, Kaphingst KA, Schofield E, Kiviniemi MT, Orom H, et al. Examining the interrelations among objective and subjective health literacy and numeracy and their associations with health knowledge. J GEN INTERN MED. 2018;33:1945\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eBegoray DL, Kwan B. A Canadian exploratory study to define a measure of health literacy. Health Promotion International. 2012;27:23\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eMarks JR, Schectman JM, Groninger H, Plews-Ogan ML. The association of health literacy and socio-demographic factors with medication knowledge. Patient Educ Couns. 2010;78:372\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eKiechle ES, Bailey SC, Hedlund LA, Viera AJ, Sheridan SL. Different measures, different outcomes? A systematic review of performance-based versus self-reported measures of health literacy and numeracy. J GEN INTERN MED. 2015;30:1538\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eHirsh JM, Boyle DJ, Collier DH, Oxenfeld AJ, Nash A, Quinzanos I, et al. Limited health literacy is a common finding in a public health hospital\u0026rsquo;s rheumatology clinic and is predictive of disease severity. J Clin Rheumatol. 2011;17:236\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003ePeters E, Bjalkebring P. Multiple numeric competencies: when a number is not just a number. Journal of Personality and Social Psychology. 2015;108:802\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eSchulz PJ, Pessina A, Hartung U, Petrocchi S. Effects of objective and subjective health literacy on patients\u0026rsquo; accurate judgment of health information and decision-making ability: survey study. Journal of Medical Internet Research. 2021;23:e20457.\u003c/li\u003e\n\u003cli\u003eMoriishi C, Takano K, Oba T, Konishi N, Katahira K, Kimura K. Factor Structure of Self-reported Health Literacy Scales: a Large-scale Cross-sectional Study.\u003c/li\u003e\n\u003cli\u003eOba T, Takano K, Katahira K, Kimura K. Exploring individual, social, and environmental factors related to physical activity: a network analysis. 2024;:2024.03.22.24304703.\u003c/li\u003e\n\u003cli\u003eOba T, Takano K, Katahira K, Kimura K. Use patterns of smartphone apps and wearable devices supporting physical activity and exercise: large-scale cross-sectional survey. JMIR mHealth and uHealth. 2023;11:e49148.\u003c/li\u003e\n\u003cli\u003eKonishi N, Oba T, Takano K, Katahira K, Kimura K. What Functions of smartphone apps and wearable devices promote physical activity? Six-month prospective study on Japanese-speaking adults. JMIR Preprints. 2024.\u003c/li\u003e\n\u003cli\u003eLee E-H, Lee YW, Lee K-W, Hong S, Kim SH. A new objective health numeracy test for patients with Type 2 Diabetes: development and evaluation of psychometric properties. Asian Nursing Research. 2020;14:66\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eNakadai K, Kasamaki J, Shimofure T. Levels of the preventive medicine and the competence measured by health literacy scale in Japan\u0026mdash;the literature review\u0026mdash;. Health and Behavior Sciences. 2018;16:73\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eOkamoto M, Kyutoku Y, Sawada M, Clowney L, Watanabe E, Dan I, et al. Health numeracy in Japan: measures of basic numeracy account for framing bias in a highly numerate population. BMC Med Inform Decis Mak. 2012;12:104.\u003c/li\u003e\n\u003cli\u003eNakayama K, Osaka W, Togari T, Ishikawa H, Yonekura Y, Sekido A, et al. Comprehensive health literacy in Japan is lower than in Europe: a validated Japanese-language assessment of health literacy. BMC Public Health. 2015;15:505.\u003c/li\u003e\n\u003cli\u003eCraig CL, Marshall AL, Sj\u0026ouml;str\u0026ouml;m M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eMurase N, Katsumura T, Ueda C, Inoue S, Shimomitsu T. Validity and reliability of Japanese version of International Physical Activity Questionnaire. Journal of Health and Welfare Statistics. 2002;49:1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eMinistry of Health, Labor and Welfare. Physical Activity Standards for Health Promotion 2013. Accessed March 15, 2024. https://www.e-healthnet.mhlw.go.jp/information/policy/guidelines_2013.html. Accessed 22 Apr 2024.\u003c/li\u003e\n\u003cli\u003eShiroiwa T, Ikeda S, Noto S, Igarashi A, Fukuda T, Saito S, et al. Comparison of value set based on DCE and/or TTO data: scoring for EQ-5D-5L health states in Japan. Value in Health. 2016;19:648\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eBonaccorsi G, Lastrucci V, Vettori V, Lorini C. Functional health literacy in a population-based sample in Florence: a cross-sectional study using the Newest Vital Sign. BMJ Open. 2019;9:e026356.\u003c/li\u003e\n\u003cli\u003eDumenci L, Matsuyama RK, Riddle DL, Cartwright L, Siminoff LA. Validation of the Cancer Health Literacy Test-30 for populations without cancer. Health Lit Res Pract. 2018;2:e58\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eCohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd edition. New York: Routledge; 1988.\u003c/li\u003e\n\u003cli\u003eCohen J. A power primer. Psychological Bulletin. 1992;112:155\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eRudolf K, Biallas B, Dejonghe LAL, Grieben C, R\u0026uuml;ckel L-M, Schaller A, et al. Influence of health literacy on the physical activity of working adults: a cross-sectional analysis of the TRISEARCH Trial. International Journal of Environmental Research and Public Health. 2019;16:4948.\u003c/li\u003e\n\u003cli\u003eMarciano L, Camerini A-L, Schulz PJ. The role of health literacy in diabetes knowledge, self-care, and glycemic control: a meta-analysis. J GEN INTERN MED. 2019;34:1007\u0026ndash;17.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e See [26] for the detailed demographic characteristics of the sample.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"objective health literacy, objective health numeracy, health-related behaviors, exploratory factor analysis","lastPublishedDoi":"10.21203/rs.3.rs-5794541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eScales for measuring health literacy and numeracy have been broadly classified into performance-based (objective) and self-reported (subjective) scales. Both types of scales have been widely used in research and practice; however, they are not always consistent and may assess different latent constructs. Furthermore, an increasing number of objective measures have been developed and it is unclear how many latent factors should be assumed. Therefore, we aimed to examine the psychometric properties and factor structure of items assessing objective health literacy across multiple scales and to clarify which aspects of objective health literacy would be correlated with subjective measures, as well as health behaviors and lifestyles.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eFive objective scales (72 items in total) were administered to Japanese-speaking adults (N\u0026thinsp;=\u0026thinsp;16,097; 7722 women; mean age\u0026thinsp;=\u0026thinsp;54.89). The analyzed scales included items assessing the numeracy, comprehension, and application of health information, some of which were contextualized for specific diseases such as diabetes and cancer. Participants\u0026rsquo; responses were submitted to exploratory factor analysis, and individual factor scores were calculated to test correlations with subjective health literacy, health behavior, and lifestyle.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eExploratory factor analysis identified three factors, which were interpreted as numeracy, comprehension, and synthesis, respectively. All numeracy items loaded onto the same factor, even when contextualized for different diseases. The comprehension factor consisted of items about medical word comprehension, and the synthesis factor was characterized by items assessing the ability to read and understand health-related information and make judgments on it using their own knowledge. The identified factors showed high inter-factor correlations (rs\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;0.67) and small-to-moderate correlations with subjective health literacy (rs\u0026thinsp;=\u0026thinsp;0.15\u0026ndash;0.44). Additionally, each factor indicated small positive correlations with healthy diet and nutrition and less substance use (rs\u0026thinsp;=\u0026thinsp;0.19\u0026ndash;0.26).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eOur findings suggest that scales of objective health literacy have at least three latent constructs (numeracy, comprehension, and synthesis) and that disease specificity is not psychometrically prominent. Each factor has some overlap with subjective health literacy, but overall, subjective and objective health literacy should be interpreted as independent constructs given the small-to-modest correlations.\u003c/p\u003e","manuscriptTitle":"Examining the Factor Structure of Objective Health Literacy and Numeracy Scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 10:46:28","doi":"10.21203/rs.3.rs-5794541/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":"e7f2453f-6f5e-4507-9e98-7c1968ff39e0","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T08:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 10:46:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794541","identity":"rs-5794541","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}