The relationship between Frailty, Health literacy, and Medication adherence in older adults

The relationship between Frailty, Health literacy, and Medication adherence in 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 The relationship between Frailty, Health literacy, and Medication adherence in older adults Elaheh Dadashi, Pouya Farokhnezhad Afshar, Hossein Matlabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background : Frailty is a significant syndrome in aging, influenced by multiple factors. Timely identification of older adults at risk of frailty and appropriate interventions can mitigate its adverse health consequences. This study examined the relationship between frailty, health literacy, and medication adherence among older adults. Method : This cross-sectional study included 210 older adults (aged 60 and above) recruited from the Tabriz Education and Training Center for Retirees. Data were collected using the Tilburg Frailty Index (TFI), Test of Functional Health Literacy in Adults (TOFHLA), the Morisky, Green and Levine (MGL) adherence scale, and a general information questionnaire. Data analysis was performed in SPSS v.27 using Spearman correlation tests, independent t-tests, ANOVA, and logistic regression analysis. Results : The mean age of the participants in this study was 68.92 ± 6.42. Of the participants, 125 (59.5%) were female and 85 (40.5%) were male, with 74.5% being married. Approximately 42% of the old participants were frail, and about 95% of them had high medication adherence. Also, 49.5% of the participants had sufficient health literacy. The findings of logistic regression analysis showed that medication adherence was significantly associated with frailty (OR = 1.37, P = 0.007), and health literacy also had an effect on frailty (OR = 0.21, P < 0.001). Conclusion : There is a relationship between medication adherence, health literacy, and frailty in older adults. Health literacy may serve as both a predictive and protective factor against frailty. Maintaining medication adherence could help improve frailty. Therefore, healthcare providers should focus on enhancing health literacy and medication adherence to prevent frailty in this population. Aged frailty Medication Adherence Health Literacy Background Aging involves the gradual decline of physiological reserves and functional abilities due to age-related changes over time [ 1 ]. The World Health Organization estimates the rapid increase of the global older adult population; their projections indicate a rise from 840 million older adults in 2013 to two billion by 2050 [ 2 ]. Approximately two-thirds of the 150,000 daily deaths worldwide occur among individuals aged 60 and older [ 3 ]. This demographic shift significantly increases the incidence and prevalence of geriatric syndromes, notably frailty. Frailty is a common geriatric syndrome [ 2 ]. Prevalence statistics vary depending on how researchers define and measure frailty, but systematic reviews show a prevalence range of 12–24% among community-dwelling older adults [ 4 ], and researchers observe it exceeding 40% in hospitalized older adults [ 5 ]. Factors such as multimorbidity and declining physical, mental, and social capacities increase the likelihood of frailty [ 6 ]. Timely identification of at-risk older adults and appropriate interventions based on predictive factors can reduce the adverse outcomes of frailty syndrome [ 2 ]. Experts consider frailty the endpoint of the health-instability spectrum [ 7 , 8 ]. Frailty, especially in older age, indicates compromised health [ 9 ]. Among the factors that affect health, we can point to health literacy [ 10 ]. Health literacy refers to an individual’s capacity to acquire, interpret, and understand health information for appropriate decision-making [ 11 ]. Studies show that health literacy is often insufficient in older adults [ 12 , 13 ]. Older adults with insufficient health literacy are one of the at-risk groups [ 14 ]. Health literacy, by definition, means the ability to read, comprehend, and act on health and medical recommendations and to adhere to treatment [ 15 ]. Treatment adherence describes the extent to which a patient’s behavior matches their medical recommendations, including medication adherence [ 16 ]. Medication adherence is a complex behavior that depends on many factors, such as individual, socioeconomic, and healthcare system factors [ 17 ]. Patient medication adherence means the patient’s commitment to following the doctor’s prescribed medication instructions, including the timely and correct administration of medications [ 18 ]. Since older adults are prone to multiple diseases, they face the risk of polypharmacy and may therefore encounter a higher risk of non-adherence compared to younger populations [ 19 ]. Studies indicate that a significant number of older adults show poor to moderate medication adherence [ 20 , 21 ]. Because non-adherence brings multiple complications, individuals with inappropriate medication adherence are likely to experience frailty to a greater extent. Therefore, we must examine these factors together to provide healthcare providers with a comprehensive understanding of an older adult’s health status and needs. This study aimed to investigate the relationship between frailty, medication adherence, and health literacy in older adults. Materials and Methods Study design This is a descriptive-analytical, cross-sectional study. The target population comprised older adults who attended the Education Retirement Center in Tabriz between summer 2024 and winter 2024. The reason for choosing this group for the study was the researcher's convenience and accessibility. Sample Participants were recruited using convenience sampling. Inclusion criteria were: age over 60 years, literacy, adequate hearing as determined by the whisper test, and intact cognitive status. The exclusion criterion was incomplete questionnaire completion. The required sample size was calculated using the following formula, with r = 0.2, β = 0.2, and α = 0.05, yielding a result of 194 participants. To achieve a statistical power of 80% with a 95% confidence level, assuming a minimum correlation coefficient of 0.2 for statistical significance, and accounting for a potential 10% rate of incomplete questionnaires, the final sample size was set to 210 participants. Data collection Data were collected using the Tilburg Frailty Indicator (TFI), a Health Literacy Assessment, the Morisky, Green and Levine adherence scale (MGL), and a demographic questionnaire. The demographic questionnaire gathered information on participants’ age, gender, education level, marital status, weight, height, monthly income, and living situation (alone, with children, or with spouse). Tilburg Frailty Indicator (TFI): The TFI was used to assess frailty in older adults [22]. This tool consists of two parts: Part A, which includes ten questions about determinants of frailty, and Part B, which includes 15 questions about the main components of frailty across three domains: social, psychological, and physical. Each question is scored as either zero or one. The physical domain contains eight questions, the psychological domain contains four questions, and the social domain contains three questions. The total score ranges from 0 to 15, with a cutoff of 5 indicating frailty (a score of 5 or higher) [23]. The Cronbach’s alpha coefficient for the Persian version of this questionnaire was reported as 0.81, and its validity was confirmed using construct validity [24]. In this study, the Cronbach’s alpha coefficient for the TFI was 0.76. Morisky, Green and Levine adherence scale (MGL): The MGL is an four-item questionnaire designed to assess medication adherence. MGL has adequate reliability and internal consistency (Cronbach alpha = 0.61) [25]. MGL includes four dichotomous questions (with “yes” or “no” answers). Its total score ranges from zero to four. Lower scores indicate a higher level of adherence. Patients' total scores can be categorized into high adherence (0 items with a "yes" answer), moderate adherence (1-2 items with a "yes" answer), and low adherence (3-4 items with a "yes" answer) [26]. United States copyright laws protect the use of the ©MGL. Professor Donald E. Morisky, who developed the MGL, permitted us to use it in this study (Supplementary material 1). In this study, the Cronbach’s alpha coefficient was 0.67. Test of Functional Health Literacy in Adults (TOFHLA): The TOFHLA includes two sections: computational and reading comprehension. The reading comprehension section tests the patient’s ability to read real-world health care texts and consists of 50 questions. The computational section includes a series of explanations regarding prescribed medications, appointment times, steps for obtaining financial assistance, and an example of a medical test result. After these explanations are provided to each individual in the form of cards, relevant questions (17 in total) are asked. Each person’s health literacy score is a number from zero to one hundred, divided into three levels: inadequate, marginal, and adequate, based on cutoff points of 59 and 74. The reliability and validation of the Persian version of the tool showed that the Cronbach’s alpha coefficient for this questionnaire is 0.84 [27]. In this study, the Cronbach’s alpha coefficient was 0.87. Data Analysis Data analysis included descriptive statistics (frequency and percentage frequency of variables, mean, and standard deviation) and inferential statistics (Kolmogorov-Smirnov test, Spearman correlation analysis, and logistic regression analysis). The collected data were analyzed using SPSS v.27. Results The participants in this study included 125 women and 85 men. The majority of participants in the research were in the 60–70 age range, and the mean age of participants was 68.92 ± 6.42. There were 123 (58.6%) non-frail and 87 (41.4%) frail. Regarding medication adherence, a large number of participants (98.5%) had high medication adherence. 59 (28.1%) participants had inadequate health literacy, 42 (19.1%) had marginal health literacy, and 109 (49.5%) had adequate health literacy (Table 1 ). Table 1 Frailty, medication adherence, and health literacy based on demographic characteristics Total TFI MGL TOFHLA variable N (%) Non frail Frail Moderate High Insufficient Border Enough Sex Male 85 (40.5) 54 (63.5) 30 (36.5) 5 (5.9) 80 (94.1) 26 (30.6) 20 (23.5) 39 (45.9) Female 125 (5.59) 69 (55.2) 56 (44.8) 4 (3.2) 121 (96.8) 33 (26.4) 22 (17.6) 70 (56.0) Results P = 0.22 Df = 1 χ 2 = 1.44 P = 0.34 Df = 1 χ 2 = 0.88 P = 0.33 Df = 2 χ 2 = 2.20 Marital status Single 8 (3.8) 7 (87.5) 1 (12.5) - 8 (100) 1 (12.5) - 7 (87.5) Married 164 (74. 5) 101 (61.6) 63 (38.4) 7 (4.3) 157 (95.7) 41 (25.0) 37 (22.6) 86 (52.4) The widow 31 (14.1) 12 (38.7) 19 (61.3) 2 (6.5) 29 (93.5) 16 (48.4) 3 (9.7) 13 (41.9) Divorced 7 (3.2) 3 (42.9) 4 (57.1) - 7 (100) 2 (28.6) 2 (28.6) 3 (42.9) Results P = 0.02 Df = 3 χ 2 = 9.12 P = 0.79 Df = 3 χ 2 = 1.02 P = 0.05 Df = 6 χ 2 = 12.50 Chronic diseases (Two or more) Yes 66 (31.4) 28 (42.4) 38 (57.6) 1 (1.5) 65 (98.5) 23 (34.8) 15 (22.7) 28 (42.4) No 144 (68.6) 95 (66.0) 49 (34.0) 8 (5.6) 136 (94.4) 36 (25.0) 27 (18.8) 81 (56.3) Results P = 0.001 Df = 1 χ 2 = 10.34 P = 0.18 Df = 1 χ 2 = 1.80 P = 0.36 Df = 4 χ 2 = 4.35 Life style (Self-report) Healthy 96 (45.7) 80 (83.3) 16 (16.7) 2 (2.1) 94 (97.9) 16 (16.7) 13 (13.5) 67 (69.8) Intermediate 104 (49.5) 40 (38.5) 64 (61.5) 7 (6.7) 97 (93.3) 40 (38.5) 27 (26.0) 37 (35.6) Unhealthy 10 (4.8) 3 (30.0) 7 (70.0) - 10 (100) 3 (30.0) 2 (20.0) 5 (50.0) Results P < 0.001 Df = 2 χ 2 = 44.95 P = 0.21 Df = 2 χ 2 = 3.09 P < 0.001 Df = 4 χ 2 = 23.57 Living arrangement Only 52 (24.7) 23 (44.2) 29 (55.8) 3 (5.8) 49 (94.2) 22 (42.3) 7 (13.5) 23 (44.2) With husband/wife 135 (64.3) 87 (64.4) 48 (35.6) 5 (3.7) 130 (96.3) 35 (25.9) 28 (20.7) 72 (53.3) With children 22 (10.5) 12 (54.4) 10 (45.5) 1 (4.5) 21 (95.5) 2 (9.1) 7 (31.8) 13 (59.1) With both 1 (0.5) 1 (100) - - 1 (100) - - 1 (100) Results P = 0.06 Df = 3 χ 2 = 7.18 P = 0.93 Df = 3 χ 2 = 0.43 P = 0.08 Df = 6 χ 2 = 11.26 χ 2 : Chi-Square; TFI: Tilburg Frailty Indicator; MGL: Morisky, Green and Levine adherence scale; TOFHLA: Test of Functional Health Literacy in Adults The results of the Kolmogorov-Smirnov test on all three variables: frailty (K-S = 0.15, P < 0.001), medication adherence (K-S = 0.11, P < 0.001), and health literacy (K-S = 0.17, P < 0.001) showed that the variables did not have a normal distribution. Therefore, Spearman's test was used to examine the correlation between them. Table 2 showed that frailty had a negative and significant correlation with health literacy; frailty also had a significant correlation with medication adherence. Table 2 Spearman Correlation between frailty, medication adherence, and health literacy Frailty Medication Adherence Health Literacy Frailty 1 r s= 0.38 P < 0.001 r s= -0.55 P < 0.001 Medication Adherence - 1 r s= -0.33 P < 0.001 Health Literacy - - 1 Table 3 showed that health literacy is able to predict frailty in older adults, and the Odds Ratio (OR) or (β) for the effect of health literacy on frailty was (OR = 0.25, P < 0.001), meaning that it can act as a reducing factor for the risk of frailty. Medication adherence also correlated with frailty according to the logistic regression test, with an odds ratio for the effect of medication adherence on frailty of (OR = 1.29, P = 0.05). The Hosmer and Lemeshow test also indicated a good fit for the model (P = 0.08). Additionally, lifestyle, marital status, and suffering from two or more chronic diseases were significantly associated with frailty, with odds ratios of (OR = 1.74) for marital status, (OR = 5.73) for lifestyle, and (OR = 0.35) for suffering from two or more chronic diseases. Table 3 Logistic Regression between Frailty, Medication Adherence, and Health Literacy Frailty Result R square Cox. R square P B constant Df Medication Adherence B 0.25 0.56 0.41 0.01 -0.67 1 EXP(β) 1.29 WALD 3.65 P 0.05 Health Literacy B -1.35 EXP(β) 0.25 WALD 33.48 P < 0.001 Marital status B 0.55 EXP(β) 1.74 WALD 4.45 P 0.03 Chronic diseases (Two or more) B -1.02 EXP(β) 0.35 WALD 6.59 P 0.01 Life style B 1.74 EXP(β) 5.73 WALD 23.00 P < 0.001 Discussion The findings of this study indicate that there is a relationship between the vulnerability of the elderly and their medication adherence and health literacy; elderly people with low levels of medication adherence and health literacy had higher vulnerability scores, and according to the tests performed and the calculation of the odds ratio, health literacy is a protective factor for vulnerability. By improving the health literacy of the elderly and medication adherence, the level of vulnerability of the elderly can be reduced. The findings indicate a significant and negative relationship between medication adherence and vulnerability, and high medication adherence in the elderly in our study. There is a study that is not consistent with our study, which did not confirm this relationship [ 28 ]. Perhaps the difference in results was due to the difference in the studied population and the difference in the instruments. However, there were studies that were consistent with our study and expressed the direct effect of weakness and medication adherence on each other [ 29 , 30 ]. Failure to take medications correctly, completely, and accurately can cause disease recurrence or failure to improve the disease. On the other hand, improper use of medications leads to drug side effects that affect all aspects of a person's physical and mental health, and through this, medication adherence can affect the vulnerability of the elderly. The high level of medication adherence in this study could be related to the fact that the study participants were in the young elderly group (60–70 years old). Health literacy had a negative and significant correlation with vulnerability in our study, and the results of logistic regression correlation showed that health literacy is a protective factor for vulnerability. Studies were in line with our study [ 31 , 32 ]. Health literacy affects decision-making about individuals' health by affecting the understanding of health information and access to health and medical services, and these decisions are related to the level of vulnerability of individuals. The high average health literacy in this study could be related to the higher number of women among the participants and the fact that individuals were in the young elderly group. According to the findings, the demographic variables of marital status, lifestyle, and having two or more diseases have a significant relationship with vulnerability. There are studies in line with our study on this issue [ 33 , 34 ]. Marital status and life composition can affect the physical, psychological, and social dimensions of vulnerability through their impact on social support, mental state, and caregiving of individuals. There is a study in line with our study that stated that lifestyle is related to vulnerability [ 35 ]. Lifestyle can affect the health of individuals through nutrition, physical activity, etc., and subsequently vulnerability. A study in line with our study was conducted that stated that vulnerability is related to chronic diseases [ 36 ]. Chronic diseases can affect the vulnerability of individuals by affecting different physical and mental dimensions of individual health. According to the results of Table 1 , gender and life composition of individuals did not have a significant relationship with any of the variables of vulnerability, medication adherence, and health literacy. Marital status was also significantly associated with vulnerability and health literacy; this may be because people who live alone have less exchange of ideas than people who live in groups, and this exchange of ideas and opinions can affect people's health literacy. Lifestyle, like marital status, was also significantly associated with vulnerability and health literacy; an individual's ability to understand and use health information significantly affects their lifestyle choices, and vice versa. Limitations We collected data from a single center, which may limit the generalizability of the findings to all older adults. Additionally, we used a convenience sampling method. Conclusion The present study’s findings indicate a significant correlation between frailty, health literacy, and medication adherence. Logistic regression analysis reveals the protective effect of health literacy against individual frailty, with individuals exhibiting lower levels of health literacy experiencing higher degrees of frailty. Therefore, it appears that enhancing the health literacy of older adults could reduce their frailty. This requires a concerted effort starting from childhood and youth to ensure individuals experience healthier aging. Additionally, increasing medication adherence levels among older adults can positively impact efforts to reduce their frailty. Abbreviations Tilburg Frailty Indicator (TFI) Morisky, Green and Levine adherence scale (MGL) Test of Functional Health Literacy in Adults (TOFHLA) Declarations Acknowledgments We sincerely thank and appreciate all the participants who helped us with their utmost patience and sense of cooperation. Clinical trial number not applicable Author contributions E.D., P.F.A., and H.M. were involved in the original conception and design of the study. E.D. and P.F.A. data collection and statistical analysis. H.M. and P.F.A. prepared the initial. All authors read and approved the final manuscript. Funding A specific project grant does not fund this study. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate The ethics committee of the Iran University of Medical Sciences approved this study (IR.IUMS.REC.1403.844). The researchers explained the study's goals. They assured participants that their personal information would be confidential. The consent that was obtained from all of the participants was informed. All research methods were conducted according to relevant guidelines and regulations, adhering to the principles of the World Medical Association’s Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors have no financial and non-financial competing interests to disclose. References Ameri GF, Govari F, Nazari T, Rashidinejad M, Afsharzadeh P: The adult age theories and definitions. Journal of Hayat 2002, 8(1):4-13. asadi h, habibi soola a, iranpour s: Prevalence of Frailty and Related Factors in the Elderly Referred to the Emergency Department of Ardabil Medical Education Centers in 2020. Journal of Gerontology 2021, 6(2):64-76. Grey ADNJd: Life Span Extension Research and Public Debate: Societal Considerations. Studies in Ethics, Law, and Technology 2007, 1(1). O’Caoimh R, Sezgin D, O’Donovan MR, Molloy DW, Clegg A, Rockwood K, Liew A: Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age and Ageing 2020, 50(1):96-104. Gómez Jiménez E, Avendaño Céspedes A, Cortés Zamora EB, García Molina R, Abizanda P: [Frailty prevalence in hospitalized older adults. A systematic review.]. Revista espanola de salud publica 2021, 95. Jafarian Yazdi A, Pashaii sabet F, Moosavi arfa N, Hasani MM: Prevalence of frailty and factors associated with frailty in the hospitalized elderly. Iranian Journal of Nursing Research 2021, 16(1):1-9. Rockwood K, Mitnitski A: Frailty in Relation to the Accumulation of Deficits. The Journals of Gerontology: Series A 2007, 62(7):722-727. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K: A standard procedure for creating a frailty index. BMC Geriatrics 2008, 8(1):24. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K: Frailty in elderly people. The Lancet 2013, 381(9868):752-762. Kobayashi LC, Wardle J, Wolf MS, von Wagner C: Aging and Functional Health Literacy: A Systematic Review and Meta-Analysis. J Gerontol B Psychol Sci Soc Sci 2016, 71(3):445-457. Mirmohammadkhani M, Ziari A, Momeni M: Systematic Review and Meta-analysis of Health Literacy in Iranian Older Adults. Salmand: Iranian Journal of Ageing 2020, 15(1):2-13. Saffari M, Sanaeinasab H, Zaboli R, Rahmati F, Ameryoun A, Lin C-Y: Sociodemographic Factors Associated with Health Literacy in General Population: A Systematic Review. Journal of Health Literacy 2025:9-26. Zamora H, Clingerman EM: Health Literacy Among Older Adults: A Systematic Literature Review. Journal of Gerontological Nursing 2011, 37(10):41-51. tamizkar p, mohammadi M, Fathnezhad-kazemi a, marami s: The relationship between health literacy level and self-care ability in the elderly. Journal of Gerontology 2019, 4(3):54-62. Kooshyar H, Shoorvazi M, Dalir Z, Hosseini M: Health Literacy and its Relationship with Medical Adherence and Health-Related Quality of Life in Diabetic Community-Residing Elderly. Journal of Mazandaran University of Medical Sciences 2013, 22(1):134-143. Julius RJ, Novitsky MAJ, Dubin WR: Medication Adherence: A Review of the Literature and Implications for Clinical Practice. Journal of Psychiatric Practice® 2009, 15(1):34-44. Ye BS, Jang EY, Kim SY, Kim E-J, Park SA, Lee Y, Hong CH, Choi SH, Yoon B, Yoon SJ: Unstable body mass index and progression to probable Alzheimer’s disease dementia in patients with amnestic mild cognitive impairment. Journal of Alzheimer's Disease 2016, 49(2):483-491. Zare S, Shams M, Fararouei M, Shariatinia S: Antihypertensive Drugs Adherence in Heart Disease Patients Referring to the Imam Reza Clinic in Shiraz. Sadra Medical Journal 2018, 6(2):151-160. Yap A, thirumoorthy t, kwan y: Medication adherence in the elderly. Journal of clinical gerontology and geriatrics 2015, 13. Fadil HA, Sammman WA, Elshafie RM: Prevalence of Nonadherence to Medications among Geriatric Patients in Al-Madinah Al-Munawara City's Hospitals, Kingdom of Saudi Arabia. Int J Clin Pract 2023, 2023:3312310. Avazeh Y, Rezaei S, Bastani P, Mehralian G: Health literacy and medication adherence in psoriasis patients: a survey in Iran. BMC Prim Care 2022, 23(1):113. Gobbens RJ, Uchmanowicz I: Assessing Frailty with the Tilburg Frailty Indicator (TFI): A Review of Reliability and Validity. Clin Interv Aging 2021, 16:863-875. Jafarian Yazdi A, Pashaee Sabet F, Farokhnezhad Afshar P: The relationship between Frailty and Quality of life in hospitalized old patient. Journal of Gerontology 2022, 7(2):1-8. Abdi M, Dabiran S, Hedayati MT, Farmani M, Abdolmaleki EY, Rahidi N: Prevalence of frailty and Prefrailty among Iraniancommunity-dwelling Older Adults. In : 2020 . Morisky DE, Green LW, Levine DM: Concurrent and predictive validity of a self-reported measure of medication adherence. Medical care 1986, 24(1):67-74. Awwad O, AlMuhaissen S, Al-Nashwan A, AbuRuz S: Translation and validation of the Arabic version of the Morisky, Green and Levine (MGL) adherence scale. PLOS ONE 2022, 17(10):e0275778. Banihashemi S-AT, Amirkhani MA, Haghdoost AA, Alavian S-M, Asgharifard H, Baradaran H, Barghamdi M, Parsinia S, Ranjbar SF: Health Literacy and the Influencing Factors: A Study in Five Provinces of Iran. Strides in Development of Medical Education 2007, 4(1):1-. Lenora Katherine frailty, medication adherence and glycemic control patients with type2 diabate. VIA MEDICA 2025, 14(VOL14,NO2(2025)). Bonikowska I, Szwamel K, Uchmanowicz I: Adherence to Medication in Older Adults with Type 2 Diabetes Living in Lubuskie Voivodeship in Poland: Association with Frailty Syndrome. J Clin Med 2022, 11(6). Qiao X, Tian X, Liu N, Dong L, Jin Y, Si H, Liu X, Wang C: The association between frailty and medication adherence among community-dwelling older adults with chronic diseases: Medication beliefs acting as mediators. Patient Educ Couns 2020. Nakamura K, Sasaki T, Yokokawa Y, Yokouchi S: Health literacy and frailty: the mediating role of instrumental activities of daily living. Psychogeriatrics 2025, 25(2):e70010. Candemir B, Yıldırım F, Yaşar E, Erten Y, Göker B: Relationship between Health Literacy and Frailty in Older Adults with Chronic Kidney Disease. Exp Aging Res 2023, 49(3):201-213. Trevisan C, Veronese N, Maggi S, Baggio G, De Rui M, Bolzetta F, Zambon S, Sartori L, Perissinotto E, Crepaldi G et al : Marital Status and Frailty in Older People: Gender Differences in the Progetto Veneto Anziani Longitudinal Study. J Womens Health (Larchmt) 2016, 25(6):630-637. Kojima G, Walters K, Iliffe S, Taniguchi Y, Tamiya N: Marital Status and Risk of Physical Frailty: A Systematic Review and Meta-analysis. Journal of the American Medical Directors Association 2020, 21(3):322-330. Mayo A, O'Brien MW, Godin J, Kehler DS, Kimmerly DS, Theou O: Can an active lifestyle offset the relationship that poor lifestyle behaviours have on frailty? Archives of Gerontology and Geriatrics 2024, 127:105556. Kyvetos A, Kyritsi E, Vrettos I, Voukelatou P, Manoli AD, Papadopoulou E, Katsaros OF, Toutouzas K: Association Between Chronic Diseases and Frailty in a Sample of Older Greek Inpatients. Cureus 2024, 16(4):e58568. Additional Declarations No competing interests reported. Supplementary Files englishmgl.pdf image2.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Editor invited by journal 09 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 03 Sep, 2025 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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abilities due to age-related changes over time [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organization estimates the rapid increase of the global older adult population; their projections indicate a rise from 840\u0026nbsp;million older adults in 2013 to two billion by 2050 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately two-thirds of the 150,000 daily deaths worldwide occur among individuals aged 60 and older [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This demographic shift significantly increases the incidence and prevalence of geriatric syndromes, notably frailty. Frailty is a common geriatric syndrome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Prevalence statistics vary depending on how researchers define and measure frailty, but systematic reviews show a prevalence range of 12\u0026ndash;24% among community-dwelling older adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and researchers observe it exceeding 40% in hospitalized older adults [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Factors such as multimorbidity and declining physical, mental, and social capacities increase the likelihood of frailty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Timely identification of at-risk older adults and appropriate interventions based on predictive factors can reduce the adverse outcomes of frailty syndrome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Experts consider frailty the endpoint of the health-instability spectrum [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Frailty, especially in older age, indicates compromised health [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Among the factors that affect health, we can point to health literacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHealth literacy refers to an individual\u0026rsquo;s capacity to acquire, interpret, and understand health information for appropriate decision-making [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies show that health literacy is often insufficient in older adults [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Older adults with insufficient health literacy are one of the at-risk groups [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Health literacy, by definition, means the ability to read, comprehend, and act on health and medical recommendations and to adhere to treatment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTreatment adherence describes the extent to which a patient\u0026rsquo;s behavior matches their medical recommendations, including medication adherence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Medication adherence is a complex behavior that depends on many factors, such as individual, socioeconomic, and healthcare system factors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Patient medication adherence means the patient\u0026rsquo;s commitment to following the doctor\u0026rsquo;s prescribed medication instructions, including the timely and correct administration of medications [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Since older adults are prone to multiple diseases, they face the risk of polypharmacy and may therefore encounter a higher risk of non-adherence compared to younger populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudies indicate that a significant number of older adults show poor to moderate medication adherence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Because non-adherence brings multiple complications, individuals with inappropriate medication adherence are likely to experience frailty to a greater extent. Therefore, we must examine these factors together to provide healthcare providers with a comprehensive understanding of an older adult\u0026rsquo;s health status and needs. This study aimed to investigate the relationship between frailty, medication adherence, and health literacy in older adults.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a descriptive-analytical, cross-sectional study. The target population comprised older adults who attended the Education Retirement Center in Tabriz between summer 2024 and winter 2024. The reason for choosing this group for the study was the researcher\u0026apos;s convenience and accessibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited using convenience sampling. Inclusion criteria were: age over 60 years, literacy, adequate hearing as determined by the whisper test, and intact cognitive status. The exclusion criterion was incomplete questionnaire completion. The required sample size was calculated using the following formula, with \u003cem\u003er\u003c/em\u003e = 0.2, \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.2, and \u003cem\u003e\u0026alpha;\u003c/em\u003e = 0.05, yielding a result of 194 participants. To achieve a statistical power of 80% with a 95% confidence level, assuming a minimum correlation coefficient of 0.2 for statistical significance, and accounting for a potential 10% rate of incomplete questionnaires, the final sample size was set to 210 participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" alt=\"image\" style=\"width: 591px; height: 111.316px;\" width=\"591\" height=\"111.316\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected using the Tilburg Frailty Indicator (TFI), a Health Literacy Assessment, the Morisky, Green and Levine adherence scale (MGL), and a demographic questionnaire. The demographic questionnaire gathered information on participants\u0026rsquo; age, gender, education level, marital status, weight, height, monthly income, and living situation (alone, with children, or with spouse).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTilburg Frailty Indicator (TFI): The TFI was used to assess frailty in older adults [22]. This tool consists of two parts: Part A, which includes ten questions about determinants of frailty, and Part B, which includes 15 questions about the main components of frailty across three domains: social, psychological, and physical. Each question is scored as either zero or one. The physical domain contains eight questions, the psychological domain contains four questions, and the social domain contains three questions. The total score ranges from 0 to 15, with a cutoff of 5 indicating frailty (a score of 5 or higher) [23]. The Cronbach\u0026rsquo;s alpha coefficient for the Persian version of this questionnaire was reported as 0.81, and its validity was confirmed using construct validity [24]. In this study, the Cronbach\u0026rsquo;s alpha coefficient for the TFI was 0.76.\u003c/li\u003e\n \u003cli\u003eMorisky, Green and Levine adherence scale (MGL): The MGL is an four-item questionnaire designed to assess medication adherence. MGL has adequate reliability and internal consistency (Cronbach alpha = 0.61) [25]. MGL includes four dichotomous questions (with \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; answers). Its total score ranges from zero to four. Lower scores indicate a higher level of adherence. Patients\u0026apos; total scores can be categorized into high adherence (0 items with a \u0026quot;yes\u0026quot; answer), moderate adherence (1-2 items with a \u0026quot;yes\u0026quot; answer), and low adherence (3-4 items with a \u0026quot;yes\u0026quot; answer) [26]. United States copyright laws protect the use of the \u0026copy;MGL. Professor Donald E. Morisky, who developed the MGL, permitted us to use it in this study (Supplementary material 1). In this study, the Cronbach\u0026rsquo;s alpha coefficient was 0.67.\u003c/li\u003e\n \u003cli\u003eTest of Functional Health Literacy in Adults (TOFHLA): The TOFHLA includes two sections: computational and reading comprehension. The reading comprehension section tests the patient\u0026rsquo;s ability to read real-world health care texts and consists of 50 questions. The computational section includes a series of explanations regarding prescribed medications, appointment times, steps for obtaining financial assistance, and an example of a medical test result. After these explanations are provided to each individual in the form of cards, relevant questions (17 in total) are asked. Each person\u0026rsquo;s health literacy score is a number from zero to one hundred, divided into three levels: inadequate, marginal, and adequate, based on cutoff points of 59 and 74. The reliability and validation of the Persian version of the tool showed that the Cronbach\u0026rsquo;s alpha coefficient for this questionnaire is 0.84 [27]. In this study, the Cronbach\u0026rsquo;s alpha coefficient was 0.87.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis included descriptive statistics (frequency and percentage frequency of variables, mean, and standard deviation) and inferential statistics (Kolmogorov-Smirnov test, Spearman correlation analysis, and logistic regression analysis). The collected data were analyzed using SPSS v.27.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe participants in this study included 125 women and 85 men. The majority of participants in the research were in the 60\u0026ndash;70 age range, and the mean age of participants was 68.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42. There were 123 (58.6%) non-frail and 87 (41.4%) frail. Regarding medication adherence, a large number of participants (98.5%) had high medication adherence. 59 (28.1%) participants had inadequate health literacy, 42 (19.1%) had marginal health literacy, and 109 (49.5%) had adequate health literacy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrailty, medication adherence, and health literacy based on demographic characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eTFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMGL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eTOFHLA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon frail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFrail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eInsufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eBorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eEnough\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003cp\u003e(40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003cp\u003e(63.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003cp\u003e(36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80\u003c/p\u003e\u003cp\u003e(94.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e26\u003c/p\u003e\u003cp\u003e(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e20\u003c/p\u003e\u003cp\u003e(23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e39\u003c/p\u003e\u003cp\u003e(45.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003cp\u003e(5.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003cp\u003e(55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003cp\u003e(44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e121\u003c/p\u003e\u003cp\u003e(96.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e33\u003c/p\u003e\u003cp\u003e(26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e22\u003c/p\u003e\u003cp\u003e(17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e70\u003c/p\u003e\u003cp\u003e(56.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.22\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(87.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164\u003c/p\u003e\u003cp\u003e(74. 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003cp\u003e(61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003cp\u003e(38.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e157\u003c/p\u003e\u003cp\u003e(95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e41\u003c/p\u003e\u003cp\u003e(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e37\u003c/p\u003e\u003cp\u003e(22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e86\u003c/p\u003e\u003cp\u003e(52.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe widow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e(14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003cp\u003e(61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e(93.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e(48.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e(41.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.79\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChronic diseases\u003c/p\u003e\u003cp\u003e(Two or more)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003cp\u003e(31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003cp\u003e(42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003cp\u003e(57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65\u003c/p\u003e\u003cp\u003e(98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23\u003c/p\u003e\u003cp\u003e(34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e(22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e28\u003c/p\u003e\u003cp\u003e(42.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003cp\u003e(68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u003c/p\u003e\u003cp\u003e(66.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003cp\u003e(34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e136\u003c/p\u003e\u003cp\u003e(94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e36\u003c/p\u003e\u003cp\u003e(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27\u003c/p\u003e\u003cp\u003e(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e81\u003c/p\u003e\u003cp\u003e(56.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.36\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLife style\u003c/p\u003e\u003cp\u003e(Self-report)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003cp\u003e(45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003cp\u003e(83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94\u003c/p\u003e\u003cp\u003e(97.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e67\u003c/p\u003e\u003cp\u003e(69.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003cp\u003e(49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64\u003c/p\u003e\u003cp\u003e(61.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97\u003c/p\u003e\u003cp\u003e(93.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27\u003c/p\u003e\u003cp\u003e(26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e37\u003c/p\u003e\u003cp\u003e(35.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnhealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e(4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;44.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.21\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;23.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLiving arrangement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003cp\u003e(24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003cp\u003e(44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e(55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49\u003c/p\u003e\u003cp\u003e(94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22\u003c/p\u003e\u003cp\u003e(42.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e23\u003c/p\u003e\u003cp\u003e(44.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith husband/wife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003cp\u003e(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003cp\u003e(64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003cp\u003e(35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e130\u003c/p\u003e\u003cp\u003e(96.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e35\u003c/p\u003e\u003cp\u003e(25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28\u003c/p\u003e\u003cp\u003e(20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e72\u003c/p\u003e\u003cp\u003e(53.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003cp\u003e(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e(45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003cp\u003e(95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e(59.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith both\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.93\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e\u003cp\u003eDf\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e: Chi-Square; TFI: Tilburg Frailty Indicator; MGL: Morisky, Green and Levine adherence scale; TOFHLA: Test of Functional Health Literacy in Adults\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the Kolmogorov-Smirnov test on all three variables: frailty (K-S\u0026thinsp;=\u0026thinsp;0.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), medication adherence (K-S\u0026thinsp;=\u0026thinsp;0.11, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and health literacy (K-S\u0026thinsp;=\u0026thinsp;0.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed that the variables did not have a normal distribution. Therefore, Spearman's test was used to examine the correlation between them.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that frailty had a negative and significant correlation with health literacy; frailty also had a significant correlation with medication adherence.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman Correlation between frailty, medication adherence, and health literacy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrailty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedication\u003c/p\u003e\u003cp\u003eAdherence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003cp\u003eLiteracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003er\u003csub\u003es=\u003c/sub\u003e 0.38\u003c/p\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003csub\u003es=\u003c/sub\u003e -0.55\u003c/p\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication Adherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003csub\u003es=\u003c/sub\u003e -0.33\u003c/p\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that health literacy is able to predict frailty in older adults, and the Odds Ratio (OR) or (β) for the effect of health literacy on frailty was (OR\u0026thinsp;=\u0026thinsp;0.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning that it can act as a reducing factor for the risk of frailty. Medication adherence also correlated with frailty according to the logistic regression test, with an odds ratio for the effect of medication adherence on frailty of (OR\u0026thinsp;=\u0026thinsp;1.29, P\u0026thinsp;=\u0026thinsp;0.05). The Hosmer and Lemeshow test also indicated a good fit for the model (P\u0026thinsp;=\u0026thinsp;0.08). Additionally, lifestyle, marital status, and suffering from two or more chronic diseases were significantly associated with frailty, with odds ratios of (OR\u0026thinsp;=\u0026thinsp;1.74) for marital status, (OR\u0026thinsp;=\u0026thinsp;5.73) for lifestyle, and (OR\u0026thinsp;=\u0026thinsp;0.35) for suffering from two or more chronic diseases.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression between Frailty, Medication Adherence, and Health Literacy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eFrailty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCox. R square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB constant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMedication Adherence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"19\" rowspan=\"20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEXP(β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHealth Literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEXP(β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEXP(β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eChronic diseases (Two or more)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEXP(β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLife style\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEXP(β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWALD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study indicate that there is a relationship between the vulnerability of the elderly and their medication adherence and health literacy; elderly people with low levels of medication adherence and health literacy had higher vulnerability scores, and according to the tests performed and the calculation of the odds ratio, health literacy is a protective factor for vulnerability. By improving the health literacy of the elderly and medication adherence, the level of vulnerability of the elderly can be reduced.\u003c/p\u003e\u003cp\u003eThe findings indicate a significant and negative relationship between medication adherence and vulnerability, and high medication adherence in the elderly in our study. There is a study that is not consistent with our study, which did not confirm this relationship [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Perhaps the difference in results was due to the difference in the studied population and the difference in the instruments. However, there were studies that were consistent with our study and expressed the direct effect of weakness and medication adherence on each other [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Failure to take medications correctly, completely, and accurately can cause disease recurrence or failure to improve the disease. On the other hand, improper use of medications leads to drug side effects that affect all aspects of a person's physical and mental health, and through this, medication adherence can affect the vulnerability of the elderly. The high level of medication adherence in this study could be related to the fact that the study participants were in the young elderly group (60\u0026ndash;70 years old).\u003c/p\u003e\u003cp\u003eHealth literacy had a negative and significant correlation with vulnerability in our study, and the results of logistic regression correlation showed that health literacy is a protective factor for vulnerability. Studies were in line with our study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Health literacy affects decision-making about individuals' health by affecting the understanding of health information and access to health and medical services, and these decisions are related to the level of vulnerability of individuals. The high average health literacy in this study could be related to the higher number of women among the participants and the fact that individuals were in the young elderly group.\u003c/p\u003e\u003cp\u003eAccording to the findings, the demographic variables of marital status, lifestyle, and having two or more diseases have a significant relationship with vulnerability. There are studies in line with our study on this issue [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Marital status and life composition can affect the physical, psychological, and social dimensions of vulnerability through their impact on social support, mental state, and caregiving of individuals. There is a study in line with our study that stated that lifestyle is related to vulnerability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Lifestyle can affect the health of individuals through nutrition, physical activity, etc., and subsequently vulnerability. A study in line with our study was conducted that stated that vulnerability is related to chronic diseases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Chronic diseases can affect the vulnerability of individuals by affecting different physical and mental dimensions of individual health.\u003c/p\u003e\u003cp\u003eAccording to the results of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, gender and life composition of individuals did not have a significant relationship with any of the variables of vulnerability, medication adherence, and health literacy. Marital status was also significantly associated with vulnerability and health literacy; this may be because people who live alone have less exchange of ideas than people who live in groups, and this exchange of ideas and opinions can affect people's health literacy. Lifestyle, like marital status, was also significantly associated with vulnerability and health literacy; an individual's ability to understand and use health information significantly affects their lifestyle choices, and vice versa.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eWe collected data from a single center, which may limit the generalizability of the findings to all older adults. Additionally, we used a convenience sampling method.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study\u0026rsquo;s findings indicate a significant correlation between frailty, health literacy, and medication adherence. Logistic regression analysis reveals the protective effect of health literacy against individual frailty, with individuals exhibiting lower levels of health literacy experiencing higher degrees of frailty.\u003c/p\u003e\u003cp\u003eTherefore, it appears that enhancing the health literacy of older adults could reduce their frailty. This requires a concerted effort starting from childhood and youth to ensure individuals experience healthier aging. Additionally, increasing medication adherence levels among older adults can positively impact efforts to reduce their frailty.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTilburg Frailty Indicator (TFI)\u003c/p\u003e\n\u003cp\u003eMorisky, Green and Levine adherence scale (MGL)\u003c/p\u003e\n\u003cp\u003eTest of Functional Health Literacy in Adults (TOFHLA)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank and appreciate all the participants who helped us with their utmost patience and sense of cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.D., P.F.A., and H.M. were involved in the original conception and design of the study. E.D. and P.F.A. data collection and statistical analysis. H.M. and P.F.A. prepared the initial. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA specific project grant does not fund this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee of the Iran University of Medical Sciences approved this study (IR.IUMS.REC.1403.844). The researchers explained the study's goals. They assured participants that their personal information would be confidential. The consent that was obtained from all of the participants was informed. All research methods were conducted according to relevant guidelines and regulations, adhering to the principles of the World Medical Association’s Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial and non-financial competing interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmeri GF, Govari F, Nazari T, Rashidinejad M, Afsharzadeh P: The adult age theories and definitions. \u003cem\u003eJournal of Hayat \u003c/em\u003e2002, 8(1):4-13.\u003c/li\u003e\n\u003cli\u003easadi h, habibi soola a, iranpour s: Prevalence of Frailty and Related Factors in the Elderly Referred to the Emergency Department of Ardabil Medical Education Centers in 2020. \u003cem\u003eJournal of Gerontology \u003c/em\u003e2021, 6(2):64-76.\u003c/li\u003e\n\u003cli\u003eGrey ADNJd: Life Span Extension Research and Public Debate: Societal Considerations. \u003cem\u003eStudies in Ethics, Law, and Technology \u003c/em\u003e2007, 1(1).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Caoimh R, Sezgin D, O\u0026rsquo;Donovan MR, Molloy DW, Clegg A, Rockwood K, Liew A: Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. \u003cem\u003eAge and Ageing \u003c/em\u003e2020, 50(1):96-104.\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez Jim\u0026eacute;nez E, Avenda\u0026ntilde;o C\u0026eacute;spedes A, Cort\u0026eacute;s Zamora EB, Garc\u0026iacute;a Molina R, Abizanda P: [Frailty prevalence in hospitalized older adults. A systematic review.]. \u003cem\u003eRevista espanola de salud publica \u003c/em\u003e2021, 95.\u003c/li\u003e\n\u003cli\u003eJafarian Yazdi A, Pashaii sabet F, Moosavi arfa N, Hasani MM: Prevalence of frailty and factors associated with frailty in the hospitalized elderly. \u003cem\u003eIranian Journal of Nursing Research \u003c/em\u003e2021, 16(1):1-9.\u003c/li\u003e\n\u003cli\u003eRockwood K, Mitnitski A: Frailty in Relation to the Accumulation of Deficits. \u003cem\u003eThe Journals of Gerontology: Series A \u003c/em\u003e2007, 62(7):722-727.\u003c/li\u003e\n\u003cli\u003eSearle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K: A standard procedure for creating a frailty index. \u003cem\u003eBMC Geriatrics \u003c/em\u003e2008, 8(1):24.\u003c/li\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K: Frailty in elderly people. \u003cem\u003eThe Lancet \u003c/em\u003e2013, 381(9868):752-762.\u003c/li\u003e\n\u003cli\u003eKobayashi LC, Wardle J, Wolf MS, von Wagner C: Aging and Functional Health Literacy: A Systematic Review and Meta-Analysis. \u003cem\u003eJ Gerontol B Psychol Sci Soc Sci \u003c/em\u003e2016, 71(3):445-457.\u003c/li\u003e\n\u003cli\u003eMirmohammadkhani M, Ziari A, Momeni M: Systematic Review and Meta-analysis of Health Literacy in Iranian Older Adults. \u003cem\u003eSalmand: Iranian Journal of Ageing \u003c/em\u003e2020, 15(1):2-13.\u003c/li\u003e\n\u003cli\u003eSaffari M, Sanaeinasab H, Zaboli R, Rahmati F, Ameryoun A, Lin C-Y: Sociodemographic Factors Associated with Health Literacy in General Population: A Systematic Review. \u003cem\u003eJournal of Health Literacy \u003c/em\u003e2025:9-26.\u003c/li\u003e\n\u003cli\u003eZamora H, Clingerman EM: Health Literacy Among Older Adults: A Systematic Literature Review. \u003cem\u003eJournal of Gerontological Nursing \u003c/em\u003e2011, 37(10):41-51.\u003c/li\u003e\n\u003cli\u003etamizkar p, mohammadi M, Fathnezhad-kazemi a, marami s: The relationship between health literacy level and self-care ability in the elderly. \u003cem\u003eJournal of Gerontology \u003c/em\u003e2019, 4(3):54-62.\u003c/li\u003e\n\u003cli\u003eKooshyar H, Shoorvazi M, Dalir Z, Hosseini M: Health Literacy and its Relationship with Medical Adherence and Health-Related Quality of Life in Diabetic Community-Residing Elderly. \u003cem\u003eJournal of Mazandaran University of Medical Sciences \u003c/em\u003e2013, 22(1):134-143.\u003c/li\u003e\n\u003cli\u003eJulius RJ, Novitsky MAJ, Dubin WR: Medication Adherence: A Review of the Literature and Implications for Clinical Practice. \u003cem\u003eJournal of Psychiatric Practice\u0026reg; \u003c/em\u003e2009, 15(1):34-44.\u003c/li\u003e\n\u003cli\u003eYe BS, Jang EY, Kim SY, Kim E-J, Park SA, Lee Y, Hong CH, Choi SH, Yoon B, Yoon SJ: Unstable body mass index and progression to probable Alzheimer\u0026rsquo;s disease dementia in patients with amnestic mild cognitive impairment. \u003cem\u003eJournal of Alzheimer\u0026apos;s Disease \u003c/em\u003e2016, 49(2):483-491.\u003c/li\u003e\n\u003cli\u003eZare S, Shams M, Fararouei M, Shariatinia S: Antihypertensive Drugs Adherence in Heart Disease Patients Referring to the Imam Reza Clinic in Shiraz. \u003cem\u003eSadra Medical Journal \u003c/em\u003e2018, 6(2):151-160.\u003c/li\u003e\n\u003cli\u003eYap A, thirumoorthy t, kwan y: Medication adherence in the elderly. \u003cem\u003eJournal of clinical gerontology and geriatrics \u003c/em\u003e2015, 13.\u003c/li\u003e\n\u003cli\u003eFadil HA, Sammman WA, Elshafie RM: Prevalence of Nonadherence to Medications among Geriatric Patients in Al-Madinah Al-Munawara City\u0026apos;s Hospitals, Kingdom of Saudi Arabia. \u003cem\u003eInt J Clin Pract \u003c/em\u003e2023, 2023:3312310.\u003c/li\u003e\n\u003cli\u003eAvazeh Y, Rezaei S, Bastani P, Mehralian G: Health literacy and medication adherence in psoriasis patients: a survey in Iran. \u003cem\u003eBMC Prim Care \u003c/em\u003e2022, 23(1):113.\u003c/li\u003e\n\u003cli\u003eGobbens RJ, Uchmanowicz I: Assessing Frailty with the Tilburg Frailty Indicator (TFI): A Review of Reliability and Validity. \u003cem\u003eClin Interv Aging \u003c/em\u003e2021, 16:863-875.\u003c/li\u003e\n\u003cli\u003eJafarian Yazdi A, Pashaee Sabet F, Farokhnezhad Afshar P: The relationship between Frailty and Quality of life in hospitalized old patient. \u003cem\u003eJournal of Gerontology \u003c/em\u003e2022, 7(2):1-8.\u003c/li\u003e\n\u003cli\u003eAbdi M, Dabiran S, Hedayati MT, Farmani M, Abdolmaleki EY, Rahidi N: Prevalence of frailty and Prefrailty among Iraniancommunity-dwelling Older Adults. In\u003cem\u003e: 2020\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eMorisky DE, Green LW, Levine DM: Concurrent and predictive validity of a self-reported measure of medication adherence. \u003cem\u003eMedical care \u003c/em\u003e1986, 24(1):67-74.\u003c/li\u003e\n\u003cli\u003eAwwad O, AlMuhaissen S, Al-Nashwan A, AbuRuz S: Translation and validation of the Arabic version of the Morisky, Green and Levine (MGL) adherence scale. \u003cem\u003ePLOS ONE \u003c/em\u003e2022, 17(10):e0275778.\u003c/li\u003e\n\u003cli\u003eBanihashemi S-AT, Amirkhani MA, Haghdoost AA, Alavian S-M, Asgharifard H, Baradaran H, Barghamdi M, Parsinia S, Ranjbar SF: Health Literacy and the Influencing Factors: A Study in Five Provinces of Iran. \u003cem\u003eStrides in Development of Medical Education \u003c/em\u003e2007, 4(1):1-.\u003c/li\u003e\n\u003cli\u003eLenora Katherine frailty, medication adherence and glycemic control patients with type2 diabate. \u003cem\u003eVIA MEDICA \u003c/em\u003e2025, 14(VOL14,NO2(2025)).\u003c/li\u003e\n\u003cli\u003eBonikowska I, Szwamel K, Uchmanowicz I: Adherence to Medication in Older Adults with Type 2 Diabetes Living in Lubuskie Voivodeship in Poland: Association with Frailty Syndrome. \u003cem\u003eJ Clin Med \u003c/em\u003e2022, 11(6).\u003c/li\u003e\n\u003cli\u003eQiao X, Tian X, Liu N, Dong L, Jin Y, Si H, Liu X, Wang C: The association between frailty and medication adherence among community-dwelling older adults with chronic diseases: Medication beliefs acting as mediators. \u003cem\u003ePatient Educ Couns \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eNakamura K, Sasaki T, Yokokawa Y, Yokouchi S: Health literacy and frailty: the mediating role of instrumental activities of daily living. \u003cem\u003ePsychogeriatrics \u003c/em\u003e2025, 25(2):e70010.\u003c/li\u003e\n\u003cli\u003eCandemir B, Yıldırım F, Yaşar E, Erten Y, G\u0026ouml;ker B: Relationship between Health Literacy and Frailty in Older Adults with Chronic Kidney Disease. \u003cem\u003eExp Aging Res \u003c/em\u003e2023, 49(3):201-213.\u003c/li\u003e\n\u003cli\u003eTrevisan C, Veronese N, Maggi S, Baggio G, De Rui M, Bolzetta F, Zambon S, Sartori L, Perissinotto E, Crepaldi G\u003cem\u003e et al\u003c/em\u003e: Marital Status and Frailty in Older People: Gender Differences in the Progetto Veneto Anziani Longitudinal Study. \u003cem\u003eJ Womens Health (Larchmt) \u003c/em\u003e2016, 25(6):630-637.\u003c/li\u003e\n\u003cli\u003eKojima G, Walters K, Iliffe S, Taniguchi Y, Tamiya N: Marital Status and Risk of Physical Frailty: A Systematic Review and Meta-analysis. \u003cem\u003eJournal of the American Medical Directors Association \u003c/em\u003e2020, 21(3):322-330.\u003c/li\u003e\n\u003cli\u003eMayo A, O\u0026apos;Brien MW, Godin J, Kehler DS, Kimmerly DS, Theou O: Can an active lifestyle offset the relationship that poor lifestyle behaviours have on frailty? \u003cem\u003eArchives of Gerontology and Geriatrics \u003c/em\u003e2024, 127:105556.\u003c/li\u003e\n\u003cli\u003eKyvetos A, Kyritsi E, Vrettos I, Voukelatou P, Manoli AD, Papadopoulou E, Katsaros OF, Toutouzas K: Association Between Chronic Diseases and Frailty in a Sample of Older Greek Inpatients. \u003cem\u003eCureus \u003c/em\u003e2024, 16(4):e58568.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aged, frailty, Medication Adherence, Health Literacy","lastPublishedDoi":"10.21203/rs.3.rs-7345167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Frailty is a significant syndrome in aging, influenced by multiple factors. Timely identification of older adults at risk of frailty and appropriate interventions can mitigate its adverse health consequences. This study examined the relationship between frailty, health literacy, and medication adherence among older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: This cross-sectional study included 210 older adults (aged 60 and above) recruited from the Tabriz Education and Training Center for Retirees. Data were collected using the Tilburg Frailty Index (TFI), Test of Functional Health Literacy in Adults (TOFHLA), the Morisky, Green and Levine (MGL) adherence scale, and a general information questionnaire. Data analysis was performed in SPSS v.27 using Spearman correlation tests, independent t-tests, ANOVA, and logistic regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mean age of the participants in this study was 68.92 ± 6.42. Of the participants, 125 (59.5%) were female and 85 (40.5%) were male, with 74.5% being married. Approximately 42% of the old participants were frail, and about 95% of them had high medication adherence. Also, 49.5% of the participants had sufficient health literacy. The findings of logistic regression analysis showed that medication adherence was significantly associated with frailty (OR = 1.37, P = 0.007), and health literacy also had an effect on frailty (OR = 0.21, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: There is a relationship between medication adherence, health literacy, and frailty in older adults. Health literacy may serve as both a predictive and protective factor against frailty. Maintaining medication adherence could help improve frailty. Therefore, healthcare providers should focus on enhancing health literacy and medication adherence to prevent frailty in this population.\u003c/p\u003e","manuscriptTitle":"The relationship between Frailty, Health literacy, and Medication adherence in older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 18:17:22","doi":"10.21203/rs.3.rs-7345167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T08:15:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T07:43:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T11:52:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115737278686197085990442131169683443963","date":"2025-10-09T08:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245782897090833386438152867860503644484","date":"2025-10-07T18:01:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26053618863718463665216908913425859815","date":"2025-10-07T14:32:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T11:58:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156275289534660456214794282016693360082","date":"2025-10-07T07:38:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T06:02:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T09:29:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T13:06:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T05:27:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-09-03T05:24:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9f9a149-62b4-4175-9cd5-1ef837f0ad28","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T14:09:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 18:17:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7345167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345167","identity":"rs-7345167","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}