Associations Between Texture-Modified Food and Functional, Systemic, and Oral Health Among Older Adults

Associations Between Texture-Modified Food and Functional, Systemic, and Oral Health Among Older Adults | 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 Associations Between Texture-Modified Food and Functional, Systemic, and Oral Health Among Older Adults Na-Young Lee, Hee Kyeong Bak, Han-Na Kim, Jung Woo Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8597405/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background Older adults frequently experience age-related declines in oral and swallowing function that increase the risk of dysphagia and its associated complications, such as aspiration pneumonia, malnutrition, and dehydration, and because of this, Texture-Modified Foods (TMFs) are widely used in clinical practice to facilitate safer oral intake and manage swallowing difficulties. This study examined the relationships between current dietary types and various health indicators in older adults residing in long-term care facilities in Korea. Method This study was conducted from March to September 2024 with older adults residing in a South Korean nursing home and an day care center. A total of 63 participants were classified into pureed/minced or regular diet groups. Key assessments included functional, systemic (sex, age, long-term care grade, activities of daily living, instrumental activities of daily living, number of medications, inbody analysis, ambulatory status, and comorbidities), and oral health status (clinical oral dryness score, tongue moisture level, tooth count, swallowing function and tongue strength). Results The findings delineate several salient predictors that distinguish the consumption of regular diets from texture-modified diets among older Korean adults. Specifically, lower activities of daily living scores, fewer medications, the presence of abdominal obesity, greater skeletal muscle mass, measurable tongue strength, and a higher number of remaining teeth were significantly associated with regular diet intake. Conclusion This study presents important evidence of correlations between TMFs and systemic and oral health variables, underscoring the clinical value of incorporating ADL, number of medications, AFR, SMI, tooth count, and tongue strength into dietary management for older adults and emphasizing the role of oral care in supporting nutritional and functional health. Activities of daily living Long-term care facilities Older adults Oral health Sarcopenia Texture-modified food Background Aging is a physiological process involving the gradual decline of bodily functions, accompanied by various physical and psychological changes. As global population aging accelerates, attention to older adult health care is increasing. South Korea, now entering a super-aged society, faces increasing urgency to improve the quality of life of older adults. With advancing age, oral functions deteriorate, reducing oral health-related quality of life and altering nutritional intake. 1 The oral musculature is involved in chewing, swallowing, and articulation, with the tongue’s muscles playing a critical role in bolus movement. Atrophy of these muscles can reduce food intake capacity, compromise nutritional status, and diminish life satisfaction. 2 Dysphagia affects approximately 8% of the global population and is increasingly prevalent with aging. Reduced masticatory function also decreases overall food intake, heightening the risk of undernutrition, infection, and various illnesses. 3 As a key aspect of oral function, masticatory ability is closely associated with quality of life and mental health. To manage dysphagia or poor mastication in older adults, texture-modified foods (TMFs) are often used instead of regular solids. 4 TMFs are typically made by adding water to adjust texture and served softened, chopped, or blended. While they aid safe swallowing, TMFs can impact nutritional status, physical function, and overall health. 5 Reduced palatability, texture changes, and poor visual appeal may lower meal satisfaction and promote negative eating attitudes. Studies show TMF users often have lower energy and protein intake, risking muscle loss. 6 Oral health, though not directly dietary, is closely linked to chewing and swallowing. 7 Softer foods may reduce chewing, accelerating muscle loss and oral decline. Studies show oral hypofunction in older adults is strongly tied to frailty and sarcopenia. 8 Thus, the link between oral function, diet, and sarcopenia must be clarified. However, research on diet modifications and oral health in Korean older adults is limited. Given cultural dietary differences, tailored studies are needed. This study aimed to analyze the relationship between diet and systemic/oral health in Korean long-term care residents and identify key factors influencing diet to guide effective nutritional strategies. Methods Participants This study was conducted from March to September 2024 with older adults residing in a South Korean nursing home and an day care center that agreed to participate. Based on a G*Power analysis 9 , the minimum required sample size was 45; with a 10% dropout margin, the target was set at 50. A total of 68 individuals enrolled (31 nursing home, 37 day care center). After one withdrawal and three exclusions due to health issues, the final sample included 63 participants. Examination Three dental hygienists with over 5 years of experience assessed participants’ general and oral health. Among them, two also held caregiver certifications. All received standardized training based on measurement manuals, and a pilot test confirmed inter-rater reliability with error rates below 10%. To maintain consistency, each dental hygienist repeatedly conducted the same set of examination items throughout the study. General and systemic characteristics Participants’ general and systemic characteristics were collected via self-reported questionnaires and institutional records, including sex, age, long-term care grade, Activities of Daily Living (ADL), Instrumental ADL (IADL), number of medications, number of illnesses InBody analysis, walking status, and systemic disease. For participants with cognitive impairment, questionnaires were completed with assistance from facility staff. Long-term care grades, assessed by the National Health Insurance Service, ranged from levels 1 to 5 and a cognitive support grade. Lower grades indicated greater physical and cognitive dependency. For analysis, participants were grouped into three categories: grades 1–2, grades 3–4, and grade 5 plus cognitive support. ADL was defined as the ability to perform essential activities for independent living(such as dressing, washing one’s face, using the toilet, etc.), whereas IADL included more complex tasks(such as grooming, preparing meals, using transportation, etc.) reflecting higher levels of autonomy. 10 It consists of a total of 15 items, and lower scores indicate higher independence. Number of medications was categorized as those taking fewer than five medications and those taking five or more medications concurrently. Body composition was assessed using the InBody S10 device (InBody Co., South Korea), suitable for seated or supine measurements. 11 Participants rested for 10–15 min to stabilize fluids. Then, Electrodes were placed on hands and feet without metal contact, using electrolyte tissues. Measurements were conducted fasting in the morning and 2 h after lunch. The following indicators were analyzed: phase angle (PA), abdominal fat ratio (AFR), visceral fat area, skeletal muscle mass index (SMI), and body mass index (BMI). Sex-specific risk variables were dichotomized as 0 (normal) or 1 (at-risk). abdominal obesity: ≥0.9 (men), ≥ 0.8 (women); low PA: ≤4.5 (men), ≤ 4.0 (women); sarcopenia: SMI < 7.0 kg/m² (men), < 5.7 kg/m² (women). 12 – 14 Walking status was categorized into two groups: the independent (walking unaided) or mobility-aided group (assistive devices) and the assistance-dependent (requiring help) or non-ambulatory group (unable to walk). Systemic diseases included representative geriatric conditions such as hypertension, diabetes, dementia, osteoporosis, stroke, coronary artery disease, heart failure, chronic obstructive pulmonary disease (COPD), cancer, and Parkinson’s disease. The presence and total number of comorbidities were recorded. Oral health status Oral health status was assessed by three trained examiners using the Clinical Oral Dryness Score (CODS), tongue moisture level, tooth count, swallowing function and tongue strength. The CODS, based on 10 indicators, was used to assess oral dryness, with scores categorized as high risk (≥ 5), caution (3–4), or normal (≤ 2). Tongue moisture was measured thrice using the Oral Moisture Checker (MCM Co., Japan). Values were categorized as normal (≥ 29.6), caution (28.0–29.5), or risk (≤ 27.9). Uncooperative participants were marked unmeasurable. 15 The tooth count was visually examined, excluding roots or teeth with mobility grade 3. Oral condition was classified by tooth count: normal (≥ 20), caution (16–19), risk (< 16). 16 Swallowing was assessed using the Modified Water Swallowing Test with 3 mL of cold water; aspiration, breathing, and voice changes were each scored 1 point, with total scores classified as normal (≤ 1) or risk (≥ 2). 17 Tongue strength was measured using a tongue pressure measurement device (TPM-02, JMS Co., Ltd., Japan). The highest of three trials was recorded and classified as normal (≥ 30 kPa) or risk (< 30 kPa). 18 However, due to cognitive impairment and difficulties in communication, only one participant could be assessed using the tongue pressure measurement device. Thus, the variable was dichotomized into measurable and unmeasurable groups. Statistical analysis TMFs were divided into two groups: Group 1 (pureed/minced) and Group 2 (regular). Pureed diets had smooth textures; minced diets included finely chopped foods; regular diets were unmodified. Several continuous variables related to general and oral health were categorized into ordinal levels (normal, caution, risk, unmeasurable). Group comparisons were conducted using t-tests and Fisher’s exact tests. To identify factors associated with TMF, multivariate binary logistic regression analysis was performed. The dependent variable was TMFs, and the independent variables included general characteristics and oral health indicators (Table 2 ). To maintain interpretability despite the large number of variables, stepwise or block-wise modeling approaches were applied. Table 2 Determinants of texture-modified food: Significant variables based on six groups Independent variable B SE Wald P OR 95% CI LLCI ULCI (1) General characteristics (Constant) 0.909 1.850 0.241 .623 2.481 ADL -0.145 0.043 11.632 .001 0.865 0.796 0.940 Abdominal fat ratio (ref. normal) 2.229 1.025 4.731 .030 9.286 1.246 69.183 (2) Systemic diseases (Constant) 5.018 5.145 0.951 .329 151.060 Hypertension 2.087 0.861 5.871 .015 8.062 1.490 43.615 Dementia -1.590 0.806 3.888 .049 0.204 0.042 0.990 (3) Oral status (Constant) 0.122 2.948 0.002 .967 1.130 Tongue strength (ref. unable to measure) 2.271 0.870 6.823 .009 9.693 1.763 53.290 Tooth count 0.058 0.030 3.834 .050 1.060 1.000 1.124 (1), (2) (Constant) 0.451 1.757 0.066 .797 1.570 ADL -0.141 0.043 10.884 .001 0.869 0.799 0.945 Number of comorbidities 2.309 0.975 5.605 .018 10.067 1.488 68.104 (1), (3) (Constant) -7.461 6.532 1.305 .253 0.001 SMI -1.874 0.940 3.975 .046 0.154 0.024 0.969 Tooth count 0.102 0.042 5.989 .014 1.107 1.020 1.201 Tongue strength (ref. unable to measure) 4.122 1.903 4.692 .030 61.689 1.480 2570.844 (2), (3) (Constant) 1.835 4.266 0.185 .667 6.267 Hypertension 1.729 0.833 4.307 .038 5.635 1.101 28.840 Tooth count 0.201 0.472 6.476 .011 1.301 1.119 1.759 Tongue strength (ref. unable to measure) 1.950 0.869 5.029 .025 7.028 1.279 38.631 Abbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval. The logistic regression analysis was structured into three primary models with sex and age included as covariates: Model 1: General characteristics and systemic health; Model 2: Systemic diseases; Model 3: Oral status (Table 2 ). Subsequently, combined multivariable models were constructed by merging model categories—(1)+(2), (1)+(3), (2)+(3), and (1)+(2)+(3). In each combined model, redundant or non-contributory variables were excluded to prevent overfitting. Across seven total models, variables that remained were refined using backward elimination to identify the optimal set of predictors. Model fit was assessed using the Hosmer–Lemeshow test, and 95% confidence intervals (CIs) were calculated. All statistical analyses were conducted using IBM SPSS Statistics 25, with statistical significance set at P < .05. Results Cross-tabulation by TMFs Cross-tabulation analysis revealed statistically significant associations between TMFs and ADL, IADL, ambulatory status, hypertension, and tongue strength. Participants in Group 1 demonstrated greater limitations in ADL, reduced mobility, and a lower rate of measurable tongue strength. General and systemic characteristics Table 1 presents the characteristics of the study participants based on TMFs. Sixty-three participants were included in the analysis and classified into two groups: Group 1 (n = 22) and Group 2 (n = 41). No significant differences were observed in sex distribution ( P = .455), age ( P = .834), and long-term care grade ( P = .192). The ADL scores were significantly higher in Group 1 than in Group 2 ( P = .001), indicating lower functional independence in Group 1. Similarly, the IADL scores were higher in Group 1 than in Group 2. Medication use and the number of systemic diseases did not differ significantly between the groups ( P = .640 and .346, respectively). Regarding InBody analysis, no significant differences were found between the normal and at-risk groups in terms of PA, AFR, and SMI ( P = .521, .933, and .780, respectively). Likewise, the BMI showed no statistically significant difference ( P = .233). However, the ambulatory status differed notably: approximately 87.8% of the participants in Group 2 could walk independently or use assistive devices, compared to 54.5% in Group 1 ( P = .003). The non-ambulatory or bedridden proportion was higher in Group 1 (45.5%) than in Group 2 (12.2%). Regarding chronic diseases, hypertension was significantly more prevalent in Group 2 than in Group 1 ( P = .04). No significant differences were found for diabetes ( P = .576), dementia ( P = .129), or Parkinson’s disease ( P = .87). Associations between TMFs: Logistic regression analysis Tables 2 and 3show the multivariate logistic regression results linking TMFs with participants’ characteristics, systemic diseases, and oral health. Factors affecting pureed versus regular diet intake were grouped into general characteristics, systemic diseases, and oral health indicators. Table 3 Determinants of texture-modified food: Final binary logistic regression using selected variables Independent variable B SE Wald P OR 95% CI LLCI ULCI (Constant) -8.414 4.834 3.029 .082 0.000 Sex (ref. woman) 2.434 1.390 3.065 .080 11.408 0.748 174.096 Age 1.135 0.921 1.520 .218 3.111 0.512 18.903 ADL -0.207 0.083 6.210 .013 0.813 0.690 0.957 Number of medications (ref. ≤5) -4.511 2.150 4.403 .036 0.011 0.000 0.743 Abdominal fat ratio (ref. normal) 3.527 1.553 5.157 .023 34.015 1.621 713.797 Phase angle (ref. normal) 2.812 1.481 3.605 .058 16.650 0.913 303.546 SMI (ref. normal) -2.774 1.250 4.926 .026 0.062 0.005 0.723 Hypertension 1.380 1.049 1.730 .188 3.975 0.509 31.073 Dementia 1.208 1.150 1.104 .293 3.348 0.351 31.905 Tongue pressure (ref. unable to measure) 4.899 2.139 5.247 .022 134.101 2.028 8868.099 Tooth count 0.140 0.053 6.866 .009 1.150 1.036 1.276 Abbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval. *P < .05 Note: Dependent variable (diet): 0 = blended diet (pureed using a blender)/minced diet (chopped into small pieces for easier chewing), 1 = regular diet (consumed in its normal form) Using backward elimination, non-significant variables were excluded from each model (Table 2), and progressively combined models were developed. A final multivariate analysis was then conducted using all retained predictors (Table 3). Multivariate logistic regression results revealed that ADL, number of medications, AFR, SMI, tongue strength, and tooth count were significantly associated with the likelihood of consuming a regular diet. Specifically, each one-point increase in ADL score reduced the odds of regular diet consumption by 18.7% (OR = 0.813, P = .013), indicating that individuals requiring more assistance with daily activities tended to consume pureed diets. Participants taking five or more medications were much less likely to consume a regular diet compared to those taking fewer than five (OR = 0.011, P = .036). Conversely, individuals with abdominal obesity were significantly more likely to consume a regular diet (OR = 34.015, P = .023). A low SMI (sarcopenia risk) was associated with lower odds of consuming a regular diet (OR = 0.062, P = .026), while each additional remaining tooth increased the odds by 15.0% (OR = 1.150, P = .009). Furthermore, individuals with measurable tongue strength were 134.1 times more likely to consume a regular diet than those without ( P = .022). Therefore, a greater likelihood of regular diet consumption was associated with lower ADL scores, taking fewer than five medications, having abdominal obesity, a normal SMI, more remaining teeth, and measurable tongue strength. In contrast, sex, age, PA, hypertension, and dementia were not significantly associated with the TMFs. Table 1. Characteristics of the study participants based on the texture-modified food Independent variable Blended/minced diet (n=22) General food (n=41) P † n (%), Mean±SD n (%), Mean±SD S ystemic status Sex .455 Men 6 (27.3) 15 (36.6) Women 16 (72.7) 26 (63.4) Age .834 90 and over 5 (22.7) 7 (17.0) 80–89 years old 13 (59.1) 28 (68.3) 70–79 years old 3 (13.6) 4 (9.8) 60–69 years old 1 (4.5) 2 (4.9) Long-term care grade .192 1, 2 5 (7.9) 5 (7.9) 3, 4 15 (23.8) 25 (39.7) 5, non-graded 2 (3.2) 11 (17.5) ADL 26.9±9.98 16.66±9.26 .001 IADL 44.55±8.93 37.72±10.94 .005 Number of medications .640 0–4 19 (86.4) 37 (90.2) 5–7 3 (13.6) 4 (9.8) Number of illnesses .346 0–3 17 (77.3) 27 (65.9) 4–5 5 (22.7) 14 (34.1) InBody analysis Phase angle normal 6 (28.6) 15 (71.4) .521 risk 14 (36.8) 24 (63.2) Abdominal fat rate normal 11 (34.3) 21 (65.6) .933 risk 9 (33.3) 18 (66.7) SMI normal 8 (24.2) 25 (75.8) .780 risk 12 (46.2) 14 (53.8) BMI 21.55±3.09 22.69±3.54 .233 Walking status .003 Able to walk, assistive device 12 (54.5) 36 (87.8) Unable to walk, bedridden 10 (45.5) 5 (12.2) Systemic diseases Hypertension .04 No 11 (50.0) 10 (24.4) Yes 11 (50.0) 31 (75.6) Diabetes .576 No 16 (72.7) 27 (65.9) Yes 6 (27.3) 14 (34.1) Dementia .129 No 4 (18.2) 15 (36.6) Yes 18 (81.8) 26 (63.4) Parkinson's disease .87 No 19 (86.4) 36 (87.8) Yes 3 (13.6) 5 (12.2) Oral status CODS .146 Normal 20 (90.9) 41 (100) Caution 1 (4.5) 0 Risk 1 (4.5) 0 Tongue moisture .227 Unable to measure 4 (18.2) 2 (3.2) Normal 5 (22.7) 17 (41.5) Caution 5 (22.7) 7 (17.1) Risk 8 (36.4) 15 (36.6) Tooth count .061 Normal 5 (22.7) 22 (53.7) Caution 1 (4.5) 1 (2.4) Risk 16 (72.7) 18 (43.9) S wallowing function 0.529 Normal 24(85.7) 31(88.6) Risk 3(10.7) 4(11.4) Unmeasurable 1(3.6) 0(0) Tongue strength 0 .004 Able to measure 14 (63.6) 38 (92.7) Unable to measure 8 (36.4) 3 (7.3) Abbreviations: ADL, activities of daily living; BMI, body mass index; CODS, clinical oral dryness score; IADL, instrumental activities of daily living; SD, standard deviation; SMI, skeletal muscle mass index Data are presented as means ± standard deviation or n (%). † Chi-squared test or t-test. † Mann–Whitney test. || Chronic Obstructive Pulmonary Disease Table 2. Determinants of texture-modified food: Significant variables based on six groups Independent variable B SE Wald P OR 95% CI LLCI ULCI (1) General characteristics (Constant) 0.909 1.850 0.241 .623 2.481 ADL -0.145 0.043 11.632 .001 0.865 0.796 0.940 Abdominal fat ratio (ref. normal) 2.229 1.025 4.731 .030 9.286 1.246 69.183 (2) Systemic diseases (Constant) 5.018 5.145 0.951 .329 151.060 Hypertension 2.087 0.861 5.871 .015 8.062 1.490 43.615 Dementia -1.590 0.806 3.888 .049 0.204 0.042 0.990 (3) Oral status (Constant) 0.122 2.948 0.002 .967 1.130 Tongue strength (ref. unable to measure) 2.271 0.870 6.823 .009 9.693 1.763 53.290 Tooth count 0.058 0.030 3.834 .050 1.060 1.000 1.124 (1), (2) (Constant) 0.451 1.757 0.066 .797 1.570 ADL -0.141 0.043 10.884 .001 0.869 0.799 0.945 Number of comorbidities 2.309 0.975 5.605 .018 10.067 1.488 68.104 (1), (3) (Constant) -7.461 6.532 1.305 .253 0.001 SMI -1.874 0.940 3.975 .046 0.154 0.024 0.969 Tooth count 0.102 0.042 5.989 .014 1.107 1.020 1.201 Tongue strength (ref. unable to measure) 4.122 1.903 4.692 .030 61.689 1.480 2570.844 (2), (3) (Constant) 1.835 4.266 0.185 .667 6.267 Hypertension 1.729 0.833 4.307 .038 5.635 1.101 28.840 Tooth count 0.201 0.472 6.476 .011 1.301 1.119 1.759 Tongue strength (ref. unable to measure) 1.950 0.869 5.029 .025 7.028 1.279 38.631 Abbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval. Note: Dependent variable (diet type): 0 = blended diet (pureed using a blender)/minced diet (chopped into small pieces for easier chewing), 1 = regular diet (consumed in its normal form). Only significant variables are presented, with sex and age included as control variables in all regression models. Variable by group: (1) type of facility, long-term care grade, ambulatory status, ADL, IADL, number of medications, number of comorbidities, phase angle, abdominal fat ratio, visceral fat ratio, SMI, and body mass index; (2) hypertension, diabetes, dementia, osteoporosis, stroke, coronary artery disease, heart failure, chronic obstructive pulmonary disease, cancer, and Parkinson’s disease; (3) tongue moisture, tongue strength, tooth count and swallowing function Discussion This study analyzed factors influencing TMF in older adults in Korean long-term care. Analysis of systemic and oral health variables identified ADL, number of medications, AFR, SMI, tongue strength, and tooth count as key determinants. These findings suggest that maintaining oral function significantly impacts the overall health of older adults and that an integrated approach combining oral function support and nutritional management is essential. Among the variables, ADL showed a strong association with TMF. ADL is consistently linked to oral and systemic health. Previous studies reported correlations between nutritional status, cognitive function, oral health, and swallowing ability, and found that improving oral condition could enhance swallowing and lower ADL scores, thereby supporting functional independence. 19,20 In this study, higher ADL scores were linked to lower chances of eating a regular diet, with each point increase reducing odds by 0.894 times. This indicates that decreased functional independence leads to greater reliance on TMFs. These findings underscore the interrelationship between swallowing function and frailty, suggesting that declines in ADL may reflect not only physical deterioration but also broader impairment in oral and swallowing functions. Reduced capacity for ADL may indicate oral functional decline beyond physical impairment, and preserving swallowing ability may help prevent ADL decline and support independence. According to the regression analysis, participants taking five or more medications were more likely to consume a pureed diet than a regular diet. In older adults, the number of medications often increases inevitably due to biological aging and the management of multiple chronic conditions. Although there is no universally standardized definition of polypharmacy, many studies define it as the use of five or more medications. 21 Systematic reviews have demonstrated a strong positive correlation between polypharmacy and frailty. 22 Moreover, a previous study reported that taking seven or more medications was associated with a 2.5-fold increased risk of developing frailty over eight years. These findings underscore the critical role of polypharmacy in the development of frailty. In addition, earlier studies reported a significant negative correlation between the number of medications and the intake of dietary fiber and vitamins. Other studies have suggested that polypharmacy contributes to cognitive decline, as well as to a range of oral health problems. 23 These findings highlight caution in multiple medication use among older adults. In older adults, abdominal obesity is often considered a marker of metabolic disorders. However, in frail older adults, weight and fat mass typically decline together, making abdominal fat a potential indicator of adequate nutritional status and energy reserves. Some studies have reported that mild to moderate adiposity in older adults can be protective, lowering mortality and morbidity. 24 Moreover, oral health issues can reduce appetite or impair chewing, triggering “anorexia of the elderly,” which may progress to malnutrition. Common markers of malnutrition include weight loss due to fat depletion and sarcopenia. Participants with more abdominal fat and no sarcopenia were likelier to eat a regular diet, supporting prior evidence that these individuals tend to have better overall nutritional status and a greater ability to maintain regular dietary intake. Additionally, since all participants in this study resided in long-term care facilities, it is likely that they received some degree of nutritional management, and thus, factors other than diet may have played a more significant role in abdominal fat accumulation. The SMI, a well-established marker of overall muscle mass and nutritional status, is also associated with weakening of tongue muscles. Sarcopenia can impair swallowing muscles, causing dysphagia, and tongue pressure testing is proposed as its clinical indicator. 25 Low tongue pressure is associated with an increased risk of aspiration pneumonia and malnutrition, prompting ongoing research in this area. In this study, participants whose tongue strength could be measured were significantly more likely to consume a regular diet than those for whom measurement was not possible. Inability to measure tongue strength may reflect broader oral and swallowing decline beyond muscle weakness. While Although tongue atrophy isn’t the only cause of dysfunction or poor nutrition, tongue muscle exercise aids recovery and helps prevent sarcopenia. 26 Previous studies have demonstrated that oral function training aimed at maintaining or improving feeding ability led to improved nutritional status among older adults. Therefore, oral exercises and rehabilitation focused on enhancing tongue strength may help improve dietary intake and overall quality of life among older adults. Masticatory function is closely associated with tooth loss, which not only reduces chewability but also leads to qualitative changes in dietary intake. As the tooth count decreases, the occlusal surface area is reduced, leading to a shift from fiber- and protein-rich foods to softer, carbohydrate-based diets, while weakened mandibular stability impairs swallowing function. 6,27 This shift can contribute to nutritional imbalance and declining systemic health. Reduced chewing was also associated with low ADL, cognitive decline, depression, and malnutrition. 7 In this study, the odds of consuming a regular diet increased by 1.1 times with each additional remaining tooth, highlighting the impact of oral health on TMF and its broader nutritional implications. According to research 28, 29 severe tooth loss or chewing and swallowing difficulties restrict food choices in older adults, leading to nutritional deficiencies, frailty, and sarcopenia. Sarcopenia, commonly observed during aging, refers to the pathological decline in muscle mass and function. Age-related sarcopenia may suppress appetite or reduce protein synthesis, thereby limiting physical activity in older adults and eventually increasing the risk of frailty, disability, and mortality. To mitigate these risks and promote healthy aging, a comprehensive approach is needed—one that supports the maintenance and recovery of oral function and encourages physical activity to help prevent the onset and progression of sarcopenia. This study had some limitations. The cross-sectional design prevented identification of changes over time or the establishment of causality. Although indicators such as SMI and nutritional status are useful, they are not sufficient to fully explain the association with TMF. It is important to recognize that the effects of TMF on overall muscle mass may vary depending on factors such as hydration status, nutrient absorption, and metabolism. 30 Moreover, due to the observational design, limited sample size, and specific study population, findings may not generalize to broader populations. Conclusion This study provides important evidence supporting correlations between TMFs and systemic and oral health variables. The findings hold clinical significance, as they emphasize the importance of considering the ADL, number of medications, AFR, SMI, tooth count, and tongue strength in the dietary management of older adults, and they highlight the role of oral care in supporting nutritional and functional health. Future research should include prospective studies to evaluate the effectiveness of intervention programs targeting these key factors. More detailed classifications of TMFs and longitudinal follow-up are also needed to clarify causal relationships. Such efforts may contribute to the development of more effective nutritional management strategies aimed at improving the health and quality of life of individuals with dysphagia. Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. The study was approved by the Institutional Review Board of Cheongju University (Approval No. 1041107-202312-BR-052-01). Written informed consent to participate was obtained from all individual participants included in the study, and for those with cognitive impairment who were unable to provide consent, written consent was obtained from their legally authorized guardians. Consent for publication Not applicable Availability of data and materials The data are not publicly available due to the inclusion of sensitive personal information of the subjects, which cannot be disclosed. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors' contributions Conceptualization, HN; Methodology, HN, NY, HK, JW; Writing—original draft, NY, HK; Investigation, HN, NY, HK; Data curation, NY, HK; Supervision, HN; All authors read and approved the final manuscript. Acknowledgements We sincerely thank the administrators, staff, and older adults at the long-term care facilities for their cooperation and participation in this study. 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J Nutr Sci Vitaminol. 2015;61(Suppl):S74–5. Kimura Y, Ogawa H, Yoshihara A, Yamaga T, Takiguchi T, Wada T, Matsubayashi K. Evaluation of chewing ability and its relationship with activities of daily living, depression, cognitive status and food intake in the community-dwelling elderly. Geriatr Gerontol Int. 2013;13(3):718–25. Yoshida M, Hiraoka A, Takeda C, Mori T, Maruyama M, Yoshikawa M, Tsuga K. Oral hypofunction and its relation to frailty and sarcopenia in community-dwelling older people. Gerodontology. 2022;39(1):26–32. Erdfelder E, Faul F, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149–60. Won CW, Yang KY, Rho YG, Kim SY, Lee EJ, Yoon JL, Lee YS. The development of Korean activities of daily living (K-ADL) and Korean instrumental activities of daily living (K-IADL) scale. J Korean Geriatr Soc. 2002;6(2):107–20. Buckinx F, Reginster JY, Dardenne N, et al. Concordance between muscle mass assessed by bioelectrical impedance analysis and by dual energy X-ray absorptiometry: a cross-sectional study. BMC Musculoskelet Disord. 2015;16:1–7. Fauziana R, Jeyagurunathan A, Abdin E, Vaingankar J, Sagayadevan V, Shafie S, Subramaniam M. Body mass index, waist-hip ratio and risk of chronic medical condition in the elderly population: results from the Well-being of the Singapore Elderly (WiSE) Study. BMC Geriatr. 2016;16:1–9. Bosy-Westphal A, Danielzik S, Dörhöfer RP, Later W, Wiese S, Müller MJ. Phase angle from bioelectrical impedance analysis: population reference values by age, sex, and body mass index. JPEN J Parenter Enter Nutr. 2006;30(4):309–16. Chen LK, Lee WJ, Peng LN, Liu LK, Arai H, Akishita M, for Sarcopenia AWG. Recent advances in sarcopenia research in Asia: 2016 update from the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2016;17(8):e7671–7. Jang JY, Lee DH. Effects of Oral Health Promotion Program on Oral Function in the Elderly. J Korean Soc Health Serv Manag. 2016;10(4):141–51. Lee JH, Yi SK, Kim SY, Kim JS, Kim HN, Jeong SH, Kim JB. Factors related to the number of existing teeth among Korean adults aged 55–79 years. Int J Environ Res Public Health. 2019;16(20):3927. Murakami K, Hirano H, Watanabe Y, et al. Relationship between swallowing function and the skeletal muscle mass of older adults requiring long-term care. Geriatr Gerontol Int. 2015;15(10):1185–92. Imamura Y, Chebib N, Ohta M, Maria Schulte-Eickhoff R, Mekki M, Schimmel M, Müller F. Validation of a novel diagnostic tool for decreased tongue pressure. J Oral Rehabil. 2021;48(11):1219–25. Furuta M, Komiya-Nonaka M, Akifusa S, Shimazaki Y, Adachi M, Kinoshita T, Yamashita Y. Interrelationship of oral health status, swallowing function, nutritional status, and cognitive ability with activities of daily living in Japanese elderly people receiving home care services due to physical disabilities. Community Dent Oral Epidemiol. 2013;41(2):173–81. Shiraisi A, Yoshimura Y, Wakabayashi H, Nagano F, Bise T, Shimazu S. Improvement in oral health enhances the recovery of activities of daily living and dysphagia after stroke. J Stroke Cerebrovasc Dis. 2021;30(9):105961. Gallagher C, et al. Polypharmacy and health outcomes in atrial fibrillation: a systematic review and meta-analysis. Open Heart. 2020;7(1):e001257. Palmer K, Villani ER, Vetrano DL, Cherubini A, Cruz-Jentoft AJ, Curtin D. Association of polypharmacy and hyperpolypharmacy with frailty states: a systematic review and meta-analysis. Eur Geriatr Med. 2019;10(1):9–36. Lelyana S, et al. Polypharmacy and oral health-related quality of life in older adults: a systematic review. Padjadjaran J Dent. 2025;37(1):97–105. Yuan L, Chang M, Wang J. Abdominal obesity, body mass index and the risk of frailty in community-dwelling older adults: a systematic review and meta-analysis. Age Ageing. 2021;50(4):1118–28. Chen KC, Lee TM, Wu WT, Wang TG, Han DS, Chang KV. Assessment of tongue strength in sarcopenia and sarcopenic dysphagia: a systematic review and meta-analysis. Front Nutr. 2021;8:684840. Tamura F, Kikutani T, Tohara T, Yoshida M, Yaegaki K. Tongue thickness relates to nutritional status in the elderly. Dysphagia. 2012;27:556–61. Shimazaki Y, Saito M, Nonoyama T, Tadokoro Y. Oral factors associated with swallowing function in independent elders. Oral Health Prev Dent. 2020;18(4):683–91. Azzolino D, Passarelli PC, De Angelis P, Piccirillo GB, D’Addona A, Cesari M. Poor oral health as a determinant of malnutrition and sarcopenia. Nutrients. 2019;11(12):2898. Wakabayashi H, Takahashi R, Watanabe N, Oritsu H, Shimizu Y. Prevalence of skeletal muscle mass loss and its association with swallowing function after cardiovascular surgery. Nutrition. 2017;38:70–3. Carretero-Krug A, Úbeda N, Velasco C, Medina-Font J, Laguna TT, Varela-Moreiras G, Montero A. Hydration status, body composition, and anxiety status in aeronautical military personnel from Spain: a cross-sectional study. Mil Med Res. 2021;8:1–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor invited by journal 11 Mar, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 21 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8597405","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610184249,"identity":"ab03c56a-03d3-4ab1-90c6-05c7f9efbd5b","order_by":0,"name":"Na-Young Lee","email":"","orcid":"","institution":"Cheongju University","correspondingAuthor":false,"prefix":"","firstName":"Na-Young","middleName":"","lastName":"Lee","suffix":""},{"id":610184250,"identity":"8628535b-dc3e-4024-9390-d54cb80faa75","order_by":1,"name":"Hee Kyeong Bak","email":"","orcid":"","institution":"Cheongju University","correspondingAuthor":false,"prefix":"","firstName":"Hee","middleName":"Kyeong","lastName":"Bak","suffix":""},{"id":610184254,"identity":"8cf9bd26-a88e-4c6d-af06-840316d3e282","order_by":2,"name":"Han-Na Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAzB5QIKBH8KXABHMxGmRbCBRC5BxACGIX4s5+9ljD36csYg2vpF87OGPCgsG/vYDzMYVeLRY9uSlG/bckMjddiMt3ZjnjASDxJkE5sQz+Bx2IMdMgucDSEuOmTRjG9AvNxiYDzbg03L+jZnkH6CWzTNyzCR//pNgkCeoBWQ4D9BhGySA1vE2SABFGJgT8Wt5YyYtc0Yid8aZZ2nSPMckeAzPJDYb4ncY0D1vjtXl9rcnH5P8UVMnJ3f88GFJfFoQQCABTPEwMDASp4GBgf8AkQpHwSgYBaNgxAEAaI9MQuixYa4AAAAASUVORK5CYII=","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Han-Na","middleName":"","lastName":"Kim","suffix":""},{"id":610184257,"identity":"8a47947f-2c84-4726-9e60-979f732a59c2","order_by":3,"name":"Jung Woo Lee","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Woo","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-01-14 04:38:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8597405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8597405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563686,"identity":"1d1637f2-1955-4679-be53-cfc8ffd50e17","added_by":"auto","created_at":"2026-03-27 12:47:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120631,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8597405/v1/4e3c020a-74c0-4354-b828-7ac5c0e7fcba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between Texture-Modified Food and Functional, Systemic, and Oral Health Among Older Adults","fulltext":[{"header":"Background","content":"\u003cp\u003eAging is a physiological process involving the gradual decline of bodily functions, accompanied by various physical and psychological changes. As global population aging accelerates, attention to older adult health care is increasing. South Korea, now entering a super-aged society, faces increasing urgency to improve the quality of life of older adults.\u003c/p\u003e \u003cp\u003eWith advancing age, oral functions deteriorate, reducing oral health-related quality of life and altering nutritional intake.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The oral musculature is involved in chewing, swallowing, and articulation, with the tongue\u0026rsquo;s muscles playing a critical role in bolus movement. Atrophy of these muscles can reduce food intake capacity, compromise nutritional status, and diminish life satisfaction.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDysphagia affects approximately 8% of the global population and is increasingly prevalent with aging. Reduced masticatory function also decreases overall food intake, heightening the risk of undernutrition, infection, and various illnesses.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e As a key aspect of oral function, masticatory ability is closely associated with quality of life and mental health.\u003c/p\u003e \u003cp\u003eTo manage dysphagia or poor mastication in older adults, texture-modified foods (TMFs) are often used instead of regular solids.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e TMFs are typically made by adding water to adjust texture and served softened, chopped, or blended. While they aid safe swallowing, TMFs can impact nutritional status, physical function, and overall health.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Reduced palatability, texture changes, and poor visual appeal may lower meal satisfaction and promote negative eating attitudes. Studies show TMF users often have lower energy and protein intake, risking muscle loss.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOral health, though not directly dietary, is closely linked to chewing and swallowing.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Softer foods may reduce chewing, accelerating muscle loss and oral decline. Studies show oral hypofunction in older adults is strongly tied to frailty and sarcopenia.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Thus, the link between oral function, diet, and sarcopenia must be clarified.\u003c/p\u003e \u003cp\u003eHowever, research on diet modifications and oral health in Korean older adults is limited. Given cultural dietary differences, tailored studies are needed. This study aimed to analyze the relationship between diet and systemic/oral health in Korean long-term care residents and identify key factors influencing diet to guide effective nutritional strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study was conducted from March to September 2024 with older adults residing in a South Korean nursing home and an day care center that agreed to participate. Based on a G*Power analysis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the minimum required sample size was 45; with a 10% dropout margin, the target was set at 50. A total of 68 individuals enrolled (31 nursing home, 37 day care center). After one withdrawal and three exclusions due to health issues, the final sample included 63 participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExamination\u003c/h3\u003e\n\u003cp\u003eThree dental hygienists with over 5 years of experience assessed participants\u0026rsquo; general and oral health. Among them, two also held caregiver certifications. All received standardized training based on measurement manuals, and a pilot test confirmed inter-rater reliability with error rates below 10%. To maintain consistency, each dental hygienist repeatedly conducted the same set of examination items throughout the study.\u003c/p\u003e\n\u003ch3\u003eGeneral and systemic characteristics\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; general and systemic characteristics were collected via self-reported questionnaires and institutional records, including sex, age, long-term care grade, Activities of Daily Living (ADL), Instrumental ADL (IADL), number of medications, number of illnesses InBody analysis, walking status, and systemic disease. For participants with cognitive impairment, questionnaires were completed with assistance from facility staff.\u003c/p\u003e \u003cp\u003eLong-term care grades, assessed by the National Health Insurance Service, ranged from levels 1 to 5 and a cognitive support grade. Lower grades indicated greater physical and cognitive dependency. For analysis, participants were grouped into three categories: grades 1\u0026ndash;2, grades 3\u0026ndash;4, and grade 5 plus cognitive support.\u003c/p\u003e \u003cp\u003eADL was defined as the ability to perform essential activities for independent living(such as dressing, washing one\u0026rsquo;s face, using the toilet, etc.), whereas IADL included more complex tasks(such as grooming, preparing meals, using transportation, etc.) reflecting higher levels of autonomy.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e It consists of a total of 15 items, and lower scores indicate higher independence.\u003c/p\u003e \u003cp\u003eNumber of medications was categorized as those taking fewer than five medications and those taking five or more medications concurrently.\u003c/p\u003e \u003cp\u003eBody composition was assessed using the InBody S10 device (InBody Co., South Korea), suitable for seated or supine measurements.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Participants rested for 10\u0026ndash;15 min to stabilize fluids. Then, Electrodes were placed on hands and feet without metal contact, using electrolyte tissues. Measurements were conducted fasting in the morning and 2 h after lunch. The following indicators were analyzed: phase angle (PA), abdominal fat ratio (AFR), visceral fat area, skeletal muscle mass index (SMI), and body mass index (BMI). Sex-specific risk variables were dichotomized as 0 (normal) or 1 (at-risk). abdominal obesity: \u0026ge;0.9 (men), \u0026ge;\u0026thinsp;0.8 (women); low PA: \u0026le;4.5 (men), \u0026le;\u0026thinsp;4.0 (women); sarcopenia: SMI\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m\u0026sup2; (men), \u0026lt;\u0026thinsp;5.7 kg/m\u0026sup2; (women).\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWalking status was categorized into two groups: the independent (walking unaided) or mobility-aided group (assistive devices) and the assistance-dependent (requiring help) or non-ambulatory group (unable to walk).\u003c/p\u003e \u003cp\u003eSystemic diseases included representative geriatric conditions such as hypertension, diabetes, dementia, osteoporosis, stroke, coronary artery disease, heart failure, chronic obstructive pulmonary disease (COPD), cancer, and Parkinson\u0026rsquo;s disease. The presence and total number of comorbidities were recorded.\u003c/p\u003e\n\u003ch3\u003eOral health status\u003c/h3\u003e\n\u003cp\u003e Oral health status was assessed by three trained examiners using the Clinical Oral Dryness Score (CODS), tongue moisture level, tooth count, swallowing function and tongue strength.\u003c/p\u003e \u003cp\u003eThe CODS, based on 10 indicators, was used to assess oral dryness, with scores categorized as high risk (\u0026ge;\u0026thinsp;5), caution (3\u0026ndash;4), or normal (\u0026le;\u0026thinsp;2).\u003c/p\u003e \u003cp\u003e Tongue moisture was measured thrice using the Oral Moisture Checker (MCM Co., Japan). Values were categorized as normal (\u0026ge;\u0026thinsp;29.6), caution (28.0\u0026ndash;29.5), or risk (\u0026le;\u0026thinsp;27.9). Uncooperative participants were marked unmeasurable.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe tooth count was visually examined, excluding roots or teeth with mobility grade 3. Oral condition was classified by tooth count: normal (\u0026ge;\u0026thinsp;20), caution (16\u0026ndash;19), risk (\u0026lt;\u0026thinsp;16).\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSwallowing was assessed using the Modified Water Swallowing Test with 3 mL of cold water; aspiration, breathing, and voice changes were each scored 1 point, with total scores classified as normal (\u0026le;\u0026thinsp;1) or risk (\u0026ge;\u0026thinsp;2).\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTongue strength was measured using a tongue pressure measurement device (TPM-02, JMS Co., Ltd., Japan). The highest of three trials was recorded and classified as normal (\u0026ge;\u0026thinsp;30 kPa) or risk (\u0026lt;\u0026thinsp;30 kPa).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e However, due to cognitive impairment and difficulties in communication, only one participant could be assessed using the tongue pressure measurement device. Thus, the variable was dichotomized into measurable and unmeasurable groups.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTMFs were divided into two groups: Group 1 (pureed/minced) and Group 2 (regular). Pureed diets had smooth textures; minced diets included finely chopped foods; regular diets were unmodified.\u003c/p\u003e \u003cp\u003eSeveral continuous variables related to general and oral health were categorized into ordinal levels (normal, caution, risk, unmeasurable). Group comparisons were conducted using t-tests and Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003cp\u003eTo identify factors associated with TMF, multivariate binary logistic regression analysis was performed. The dependent variable was TMFs, and the independent variables included general characteristics and oral health indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To maintain interpretability despite the large number of variables, stepwise or block-wise modeling approaches were applied.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of texture-modified food: Significant variables based on six groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(1) General characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbdominal fat ratio (ref. normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(2) Systemic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(3) Oral status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTooth count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(1), (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(1), (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTooth count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2570.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(2), (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTooth count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe logistic regression analysis was structured into three primary models with sex and age included as covariates: Model 1: General characteristics and systemic health; Model 2: Systemic diseases; Model 3: Oral status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, combined multivariable models were constructed by merging model categories\u0026mdash;(1)+(2), (1)+(3), (2)+(3), and (1)+(2)+(3). In each combined model, redundant or non-contributory variables were excluded to prevent overfitting. Across seven total models, variables that remained were refined using backward elimination to identify the optimal set of predictors. Model fit was assessed using the Hosmer\u0026ndash;Lemeshow test, and 95% confidence intervals (CIs) were calculated. All statistical analyses were conducted using IBM SPSS Statistics 25, with statistical significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eCross-tabulation by TMFs\u003c/h2\u003e\n \u003cp\u003eCross-tabulation analysis revealed statistically significant associations between TMFs and ADL, IADL, ambulatory status, hypertension, and tongue strength. Participants in Group 1 demonstrated greater limitations in ADL, reduced mobility, and a lower rate of measurable tongue strength.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGeneral and systemic characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;1 presents the characteristics of the study participants based on TMFs. Sixty-three participants were included in the analysis and classified into two groups: Group 1 (n = 22) and Group 2 (n = 41). No significant differences were observed in sex distribution (\u003cem\u003eP\u003c/em\u003e = .455), age (\u003cem\u003eP\u003c/em\u003e = .834), and long-term care grade (\u003cem\u003eP\u003c/em\u003e = .192).\u003c/p\u003e\n\u003cp\u003eThe ADL scores were significantly higher in Group 1 than in Group 2 (\u003cem\u003eP\u003c/em\u003e = .001), indicating lower functional independence in Group 1. Similarly, the IADL scores were higher in Group 1 than in Group 2. Medication use and the number of systemic diseases did not differ significantly between the groups (\u003cem\u003eP\u003c/em\u003e = .640 and .346, respectively).\u003c/p\u003e\n\u003cp\u003eRegarding InBody analysis, no significant differences were found between the normal and at-risk groups in terms of PA, AFR, and SMI (\u003cem\u003eP\u003c/em\u003e = .521, .933, and .780, respectively). Likewise, the BMI showed no statistically significant difference (\u003cem\u003eP\u003c/em\u003e = .233).\u003c/p\u003e\n\u003cp\u003eHowever, the ambulatory status differed notably: approximately 87.8% of the participants in Group 2 could walk independently or use assistive devices, compared to 54.5% in Group 1 (\u003cem\u003eP\u003c/em\u003e = .003). The non-ambulatory or bedridden proportion was higher in Group 1 (45.5%) than in Group 2 (12.2%).\u003c/p\u003e\n\u003cp\u003eRegarding chronic diseases, hypertension was significantly more prevalent in Group 2 than in Group 1 (\u003cem\u003eP\u003c/em\u003e = .04). No significant differences were found for diabetes (\u003cem\u003eP\u003c/em\u003e = .576), dementia (\u003cem\u003eP\u003c/em\u003e = .129), or Parkinson’s disease (\u003cem\u003eP\u003c/em\u003e = .87).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eAssociations between TMFs: Logistic regression analysis\u003c/h2\u003e\n \u003cp\u003eTables 2 and 3show the multivariate logistic regression results linking TMFs with participants’ characteristics, systemic diseases, and oral health. Factors affecting pureed versus regular diet intake were grouped into general characteristics, systemic diseases, and oral health indicators.\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDeterminants of texture-modified food: Final binary logistic regression using selected variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-8.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex (ref. woman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e174.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e18.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNumber of medications (ref. ≤5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-4.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAbdominal fat ratio\u003c/p\u003e\n \u003cp\u003e(ref. normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e34.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e713.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhase angle\u003c/p\u003e\n \u003cp\u003e(ref. normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e16.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e303.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSMI\u003c/p\u003e\n \u003cp\u003e(ref. normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-2.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTongue pressure\u003c/p\u003e\n \u003cp\u003e(ref. unable to measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e134.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8868.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTooth count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003e.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003e*P\u003c/em\u003e \u0026lt; .05\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eNote: Dependent variable (diet): 0 = blended diet (pureed using a blender)/minced diet (chopped into small pieces for easier chewing), 1 = regular diet (consumed in its normal form)\u003c/p\u003e\n \u003cp\u003eUsing backward elimination, non-significant variables were excluded from each model (Table 2), and progressively combined models were developed. A final multivariate analysis was then conducted using all retained predictors (Table 3).\u003c/p\u003e\n \u003cp\u003eMultivariate logistic regression results revealed that ADL, number of medications, AFR, SMI, tongue strength, and tooth count were significantly associated with the likelihood of consuming a regular diet. Specifically, each one-point increase in ADL score reduced the odds of regular diet consumption by 18.7% (OR = 0.813, \u003cem\u003eP\u003c/em\u003e = .013), indicating that individuals requiring more assistance with daily activities tended to consume pureed diets. Participants taking five or more medications were much less likely to consume a regular diet compared to those taking fewer than five (OR = 0.011, \u003cem\u003eP\u003c/em\u003e = .036). Conversely, individuals with abdominal obesity were significantly more likely to consume a regular diet (OR = 34.015, \u003cem\u003eP\u003c/em\u003e = .023).\u003c/p\u003e\n \u003cp\u003eA low SMI (sarcopenia risk) was associated with lower odds of consuming a regular diet (OR = 0.062, \u003cem\u003eP\u003c/em\u003e = .026), while each additional remaining tooth increased the odds by 15.0% (OR = 1.150, \u003cem\u003eP\u003c/em\u003e = .009). Furthermore, individuals with measurable tongue strength were 134.1 times more likely to consume a regular diet than those without (\u003cem\u003eP\u003c/em\u003e = .022).\u003c/p\u003e\n \u003cp\u003eTherefore, a greater likelihood of regular diet consumption was associated with lower ADL scores, taking fewer than five medications, having abdominal obesity, a normal SMI, more remaining teeth, and measurable tongue strength. In contrast, sex, age, PA, hypertension, and dementia were not significantly associated with the TMFs.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;1. Characteristics of the study participants based on the texture-modified food\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlended/minced diet (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral food (n=41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e†\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003en (%), Mean±SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003en (%), Mean±SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003eystemic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e90 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e80–89 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e70–79 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e60–69 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong-term care grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1, 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e3, 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e5, non-graded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.9±9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.66±9.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.55±8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.72±10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of medications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0–4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e5–7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of illnesses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0–3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e4–5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInBody analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePhase angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAbdominal fat rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.55±3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.69±3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWalking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAble to walk, assistive device\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnable to walk, bedridden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystemic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDementia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParkinson's disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOral status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCODS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCaution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTongue moisture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnable to measure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCaution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTooth count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCaution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003ewallowing function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31(88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4(11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnmeasurable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTongue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estrength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAble to measure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnable to measure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ADL, activities of daily living; BMI, body mass index; CODS, clinical oral dryness score; IADL, instrumental activities of daily living; SD, standard deviation; SMI, skeletal muscle mass index\u003c/p\u003e\n\u003cp\u003eData are presented as means ± standard deviation or n (%).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e†\u003c/sup\u003e Chi-squared test or t-test. \u003csup\u003e†\u003c/sup\u003e Mann–Whitney test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e||\u003c/sup\u003e Chronic Obstructive Pulmonary Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Determinants of texture-modified food: Significant variables based on six groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e(1) General characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbdominal fat ratio (ref. normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e(2) Systemic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e151.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e(3) Oral status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTooth count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e(1), (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e(1), (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTooth count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2570.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e(2), (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTooth count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTongue strength (ref. unable to measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: ADL, activities of daily living; CI, confidence interval; LLCI, lower limit of confidence interval; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; ULCI, upper limit of confidence interval.\u003c/p\u003e\n\u003cp\u003eNote: Dependent variable (diet type): 0 = blended diet (pureed using a blender)/minced diet (chopped into small pieces for easier chewing), 1 = regular diet (consumed in its normal form). Only significant variables are presented, with sex and age included as control variables in all regression models.\u003c/p\u003e\n\u003cp\u003eVariable by group: (1) type of facility, long-term care grade, ambulatory status, ADL, IADL, number of medications, number of comorbidities, phase angle, abdominal fat ratio, visceral fat ratio, SMI, and body mass index; (2) hypertension, diabetes, dementia, osteoporosis, stroke, coronary artery disease, heart failure, chronic obstructive pulmonary disease, cancer, and Parkinson’s disease; (3) tongue moisture, tongue strength, tooth count and swallowing function\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed factors influencing TMF in older adults in Korean long-term care. Analysis of systemic and oral health variables identified ADL, number of medications, AFR, SMI, tongue strength, and tooth count as key determinants. These findings suggest that maintaining oral function significantly impacts the overall health of older adults and that an integrated approach combining oral function support and nutritional management is essential.\u003c/p\u003e\n\u003cp\u003eAmong the variables, ADL showed a strong association with TMF. ADL is consistently linked to oral and systemic health. Previous studies reported correlations between nutritional status, cognitive function, oral health, and swallowing ability, \u003cs\u003eand found\u003c/s\u003e that improving oral condition could enhance swallowing and lower ADL scores, thereby supporting functional independence.\u003csup\u003e19,20\u003c/sup\u003e In this study, higher ADL scores were linked to lower chances of eating a regular diet, with each point increase reducing odds by 0.894 times. This indicates that decreased functional independence leads to greater reliance on\u0026nbsp;TMFs. These findings underscore the interrelationship between swallowing function and frailty, suggesting that declines in ADL may reflect not only physical deterioration but also broader impairment in oral and swallowing functions. Reduced capacity for\u0026nbsp;ADL\u0026nbsp;may indicate oral functional decline beyond physical impairment, and preserving swallowing ability may help prevent ADL decline and support independence.\u003c/p\u003e\n\u003cp\u003eAccording to the regression analysis, participants taking five or more medications were more likely to consume a pureed diet than a regular diet. In older adults, the number of medications often increases inevitably due to biological aging and the management of multiple chronic conditions. Although there is no universally standardized definition of polypharmacy, many studies define it as the use of five or more medications.\u003csup\u003e21\u003c/sup\u003e Systematic reviews have demonstrated a strong positive correlation between polypharmacy and frailty.\u003csup\u003e22\u003c/sup\u003e Moreover, a previous study reported that taking seven or more medications was associated with a 2.5-fold increased risk of developing frailty over eight years. These findings underscore the critical role of polypharmacy in the development of frailty. In addition, earlier studies reported a significant negative correlation between the number of medications and the intake of dietary fiber and vitamins. Other studies have suggested that polypharmacy contributes to cognitive decline, as well as to a range of oral health problems.\u003csup\u003e23\u003c/sup\u003e These findings highlight caution in multiple medication use among older adults.\u003c/p\u003e\n\u003cp\u003eIn older adults, abdominal obesity is often considered a marker of metabolic disorders. However, in frail older adults, weight and fat mass typically decline together, making abdominal fat a potential indicator of adequate nutritional status and energy reserves. Some studies have reported that mild to moderate adiposity in older adults can be protective, lowering mortality and morbidity.\u003csup\u003e24\u003c/sup\u003e Moreover, oral health issues can reduce appetite or impair chewing, triggering \u0026ldquo;anorexia of the elderly,\u0026rdquo; which may progress to malnutrition. Common markers of malnutrition include weight loss due to fat depletion and sarcopenia. Participants with more abdominal fat and no sarcopenia were likelier to eat a regular diet, supporting prior evidence that these individuals tend to have better overall nutritional status and a greater ability to maintain regular dietary intake. Additionally, since all participants in this study resided in long-term care facilities, it is likely that they received some degree of nutritional management, and thus, factors other than diet may have played a more significant role in abdominal fat accumulation.\u003c/p\u003e\n\u003cp\u003eThe SMI, a well-established marker of overall muscle mass and nutritional status, is also associated with weakening of tongue muscles. Sarcopenia can impair swallowing muscles, causing dysphagia, and tongue pressure testing is proposed as its clinical indicator.\u003csup\u003e25\u0026nbsp;\u003c/sup\u003eLow tongue pressure is associated with an increased risk of aspiration pneumonia and malnutrition, prompting ongoing research in this area. In this study, participants whose tongue strength could be measured were significantly more likely to consume a regular diet than those for whom measurement was not possible. Inability to measure tongue strength may reflect broader oral and swallowing decline beyond muscle weakness. While Although tongue atrophy isn\u0026rsquo;t the only cause of dysfunction or poor nutrition, tongue muscle exercise aids recovery and helps prevent sarcopenia.\u003csup\u003e26\u003c/sup\u003e Previous studies have\u0026nbsp;demonstrated that oral function training aimed at maintaining or improving feeding ability led to improved nutritional status among older adults. Therefore, oral exercises and rehabilitation focused on enhancing tongue strength may help improve dietary intake and overall quality of life among older adults.\u003c/p\u003e\n\u003cp\u003eMasticatory function is closely associated with tooth loss, which not only reduces chewability but also leads to qualitative changes in dietary intake. As the tooth count decreases, the occlusal surface area is reduced, leading to a shift from fiber- and protein-rich foods to softer, carbohydrate-based diets, while weakened mandibular stability impairs swallowing function.\u003csup\u003e6,27\u003c/sup\u003e This shift can contribute to nutritional imbalance and declining systemic health. Reduced chewing was also associated with low ADL, cognitive decline, depression, and malnutrition.\u003csup\u003e7\u003c/sup\u003e In this study, the odds of consuming a regular diet increased by 1.1 times with each additional remaining tooth, highlighting the impact of oral health on TMF and its broader nutritional implications. According to research\u003csup\u003e28,\u0026nbsp;29\u003c/sup\u003e severe tooth loss or chewing and swallowing difficulties restrict food choices in older adults, leading to nutritional deficiencies, frailty, and sarcopenia. Sarcopenia, commonly observed during aging, refers to the pathological decline in muscle mass and function. Age-related sarcopenia may suppress appetite or reduce protein synthesis, thereby limiting physical activity in older adults and eventually increasing the risk of frailty, disability, and mortality. To mitigate these risks and promote healthy aging, a comprehensive approach is needed\u0026mdash;one that supports the maintenance and recovery of oral function and encourages physical activity to help prevent the onset and progression of sarcopenia.\u003c/p\u003e\n\u003cp\u003eThis study had some limitations. The cross-sectional design prevented identification of changes over time or the establishment of causality. Although indicators such as SMI and nutritional status are useful, they are not sufficient to fully explain the association with TMF. It is important to recognize that the effects of TMF on overall muscle mass may vary depending on factors such as hydration status, nutrient absorption, and metabolism.\u003csup\u003e30\u003c/sup\u003e Moreover, due to the observational design, limited sample size, and specific study population, findings may not generalize to broader populations. \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides important evidence supporting correlations between TMFs and systemic and oral health variables. The findings hold clinical significance, as they emphasize the importance of considering the ADL, number of medications, AFR, SMI, tooth count, and tongue strength in the dietary management of older adults, and they highlight the role of oral care in supporting nutritional and functional health.\u003c/p\u003e\n\u003cp\u003eFuture research should include prospective studies to evaluate the effectiveness of intervention programs targeting these key factors. More detailed classifications of TMFs and longitudinal follow-up are also needed to clarify causal relationships. Such efforts may contribute to the development of more effective nutritional management strategies aimed at improving the health and quality of life of individuals with dysphagia.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. The study was approved by the Institutional Review Board of Cheongju University (Approval No. 1041107-202312-BR-052-01). Written informed consent to participate was obtained from all individual participants included in the study, and for those with cognitive impairment who were unable to provide consent, written consent was obtained from their legally authorized guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to the inclusion of sensitive personal information of the subjects, which cannot be disclosed.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, HN; Methodology, HN, NY, HK, JW; Writing\u0026mdash;original draft, NY, HK; Investigation, HN, NY, HK; Data curation, NY, HK; Supervision, HN; All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the administrators, staff, and older adults at the long-term care facilities for their cooperation and participation in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e \u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKossioni AE. The association of poor oral health parameters with malnutrition in older adults: a review considering the potential implications for cognitive impairment. Nutrients. 2018;10(11):1709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandelman D, Petersen PE, Ueda H. Oral health, general health, and quality of life in older people. Spec Care Dentist. 2008;28(6):224\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayasinghe TN, Harrass S, Erdrich S, King S, Eberhard J. Protein intake and oral health in older adults\u0026mdash;A narrative review. Nutrients. 2022;14(21):4478.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCichero JAY. Texture-modified meals for hospital patients. In: Chen J, Rosenthal A, editors. Modifying Food Texture. Cambridge, UK: Woodhead Publishing; 2015. pp. 135\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu A, Fujishima I, Maeda K, Murotani K, Kayashita J, Ohno T, Mori N. Texture-modified diets are associated with poor appetite in older adults who are admitted to a post-acute rehabilitation hospital. J Am Med Dir Assoc. 2021;22(9):1960\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkebe K. Significance of oral function for dietary intakes in old people. J Nutr Sci Vitaminol. 2015;61(Suppl):S74\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimura Y, Ogawa H, Yoshihara A, Yamaga T, Takiguchi T, Wada T, Matsubayashi K. Evaluation of chewing ability and its relationship with activities of daily living, depression, cognitive status and food intake in the community-dwelling elderly. Geriatr Gerontol Int. 2013;13(3):718\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida M, Hiraoka A, Takeda C, Mori T, Maruyama M, Yoshikawa M, Tsuga K. Oral hypofunction and its relation to frailty and sarcopenia in community-dwelling older people. Gerodontology. 2022;39(1):26\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdfelder E, Faul F, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWon CW, Yang KY, Rho YG, Kim SY, Lee EJ, Yoon JL, Lee YS. The development of Korean activities of daily living (K-ADL) and Korean instrumental activities of daily living (K-IADL) scale. J Korean Geriatr Soc. 2002;6(2):107\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckinx F, Reginster JY, Dardenne N, et al. 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Oral factors associated with swallowing function in independent elders. Oral Health Prev Dent. 2020;18(4):683\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzzolino D, Passarelli PC, De Angelis P, Piccirillo GB, D\u0026rsquo;Addona A, Cesari M. Poor oral health as a determinant of malnutrition and sarcopenia. Nutrients. 2019;11(12):2898.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakabayashi H, Takahashi R, Watanabe N, Oritsu H, Shimizu Y. Prevalence of skeletal muscle mass loss and its association with swallowing function after cardiovascular surgery. Nutrition. 2017;38:70\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarretero-Krug A, \u0026Uacute;beda N, Velasco C, Medina-Font J, Laguna TT, Varela-Moreiras G, Montero A. Hydration status, body composition, and anxiety status in aeronautical military personnel from Spain: a cross-sectional study. Mil Med Res. 2021;8:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Activities of daily living, Long-term care facilities, Older adults, Oral health, Sarcopenia, Texture-modified food","lastPublishedDoi":"10.21203/rs.3.rs-8597405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8597405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOlder adults frequently experience age-related declines in oral and swallowing function that increase the risk of dysphagia and its associated complications, such as aspiration pneumonia, malnutrition, and dehydration, and because of this, Texture-Modified Foods (TMFs) are widely used in clinical practice to facilitate safer oral intake and manage swallowing difficulties. This study examined the relationships between current dietary types and various health indicators in older adults residing in long-term care facilities in Korea.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study was conducted from March to September 2024 with older adults residing in a South Korean nursing home and an day care center. A total of 63 participants were classified into pureed/minced or regular diet groups. Key assessments included functional, systemic (sex, age, long-term care grade, activities of daily living, instrumental activities of daily living, number of medications, inbody analysis, ambulatory status, and comorbidities), and oral health status (clinical oral dryness score, tongue moisture level, tooth count, swallowing function and tongue strength).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings delineate several salient predictors that distinguish the consumption of regular diets from texture-modified diets among older Korean adults. Specifically, lower activities of daily living scores, fewer medications, the presence of abdominal obesity, greater skeletal muscle mass, measurable tongue strength, and a higher number of remaining teeth were significantly associated with regular diet intake.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study presents important evidence of correlations between TMFs and systemic and oral health variables, underscoring the clinical value of incorporating ADL, number of medications, AFR, SMI, tooth count, and tongue strength into dietary management for older adults and emphasizing the role of oral care in supporting nutritional and functional health.\u003c/p\u003e","manuscriptTitle":"Associations Between Texture-Modified Food and Functional, Systemic, and Oral Health Among Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 08:22:16","doi":"10.21203/rs.3.rs-8597405/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-24T09:09:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T18:00:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T00:18:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259667727463149981836953957501022007247","date":"2026-03-31T07:33:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T05:25:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207765358081739548159947379366947566384","date":"2026-03-30T04:58:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77537357054861937659024530790705134238","date":"2026-03-29T13:05:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283108759618982895909545069860711152773","date":"2026-03-29T10:38:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145876779410625827049204993198250254450","date":"2026-03-25T03:12:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T11:36:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T04:49:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T04:30:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T03:36:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2026-01-22T03:31:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"959d08b8-f3c6-41a4-a4f7-baf723836251","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T09:46:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 08:22:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8597405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8597405","identity":"rs-8597405","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}