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Regional differences in frailty among older adults with type 2 diabetes: a multicenter cross-sectional study in Japan

BMC Geriatrics volume 24, Article number: 688 (2024) Cite this article

Abstract

Background

Social environment may broadly impact multifaceted frailty; however, how environmental differences influence frailty in older adults with diabetes remains unclear. This study aimed to investigate regional differences in frailty in urban and rural areas among older adults with diabetes.

Methods

This cross-sectional study was conducted as part of the frailty prevention program for older adults with diabetes study. Older adults aged 60–80 years who could independently perform basic activities of daily living (ADLs) were enrolled sequentially. Trained nurses obtained patient background, complications, body weight, body composition, blood tests, grip strength, frailty assessment, and self-care score results. Regional differences in frailty were evaluated using logistic and multiple linear regression analyses.

Results

This study included 417 participants (269 urban and 148 rural). The prevalence of robustness was significantly lower in rural areas than in urban areas (29.7% vs. 43.9%, p = 0.018). Living in rural areas was associated with frailty (odds ratio [OR] 2.55, 95% confidence interval [CI] 1.38–4.71) and pre-frailty (OR 2.10, 95%CI 1.30–3.41). Lower instrumental ADL (B 0.28, standard error [SE] 0.073) and social ADL (B 0.265, SE 0.097) were characteristics of rural residents.

Conclusions

Regional differences in frailty were observed. Older adults with diabetes living in rural areas have a higher risk of frailty owing to a decline in instrumental and social ADLs. Social environment assessment and intervention programs that include communication strategies to enable care and social participation across environments are crucial to the effective and early prevention of frailty.

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Background

Frailty is “a clinical state in which there is an increase in an individual’s vulnerability to dependency and mortality when exposed to a stressor” [1, 2]. Frailty has been associated with mortality, reduced activities of daily living (ADLs), hospitalization, physical limitations, falls, and fractures [3]. It is an intermediate stage between being healthy and needing care [4, 5] and could be treated using appropriate interventions, especially in the early stages [6, 7]. Therefore, early assessments to detect frailty and interventions based on these assessments are required to ensure healthy aging [5, 7, 8].

Diabetes mellitus is one of the most common risk factors for frailty [9,10,11]. In addition, the combination of diabetes and frailty further worsens prognoses, leading to the development of physical dysfunction and mortality [12,13,14]. Therefore, identifying diabetes-specific factors that influence the development of frailty in older adults with diabetes is crucial to enabling more effective assessments and interventions based on this unique condition of older adults with diabetes.

The risk factors for frailty include a wide range of demographic, social, clinical, lifestyle, and biological factors [7, 15]. However, most previous studies on frailty have focused on the physical aspects using previously reported assessment methods [16,17,18]. Few studies have examined the risk of frailty in detail, particularly among older adults with diabetes. Therefore, we previously reported multifaceted risk factors associated with diabetes-specific frailty in older adults who could independently perform ADL [19]. The study showed gender differences in these factors, indicating the need to consider gender in preventive interventions. In addition, diabetes medications also influence frailty, particularly in older adults receiving sodium-glucose co-transporter-2 inhibitors (SGLT2i), in whom frailty has already developed, even though they appear to be healthy. Consequently, it is necessary to pay attention to frailty at the time of drug administration [20, 21].

These studies have evaluated various factors; however, regional differences have not been adequately explored owing to the limitations of the study design. Previous studies have reported the regional incidence of frailty in older adults [22, 23]; nevertheless, no study has examined the regional differences in the risk of frailty and its characteristics among older adults with diabetes. If regional differences in frailty are clarified, more effective diabetes care can be proposed for older adults to prevent frailty and manage diabetes. Therefore, this study aimed to investigate the regional differences in frailty between urban and rural areas among older adults with diabetes using a national multicenter survey.

Methods

Study design and participants

This cross-sectional study was conducted as part of the frailty prevention program for older adults with diabetes (The f-PPOD) study. This protocol has been previously reported [21]. The inclusion criteria were the presence of type 2 diabetes, age 60–80 years, and unimpaired basic ADL (Barthel index ≥ 85). The exclusion criteria were certification for long-term care/support needs and presence of cerebrovascular and peripheral artery diseases, paralysis in any part of the body, severe diabetic complications, including macrovascular diseases, comorbidities (heart failure, liver and renal disorders, anemia, malignancy, and dementia), and depression or other psychiatric problems. Information on diabetic complications was collected from medical records for peripheral neuropathy, retinopathy, nephropathy, autonomic neuropathy, peripheral arterial disease, and cardiovascular disease, based on annual tests according to guidelines. The physician in charge diagnosed the severity of diabetic complications according to the severity criteria for each condition.

The study participants were recruited between March 21, 2017, and February 7, 2020, from eight outpatient diabetes clinics in Japan. Since this study was a multi-purpose exploratory study on frailty in older adults with diabetes, the sample size was calculated to include 10% (approximately 400 people) of outpatients who met the eligibility criteria at each hospital and clinic. After trained assessors (certified diabetes educators, certified nurses in diabetes nursing, and a diabetologist) screened medical records for eligibility, all participants were sequentially recruited upon arrival at the outpatient clinics.

Instrument

Participants’ demographic characteristics

Demographic characteristics, including age, gender, academic background, family structure, work status, irregular lifestyle (irregular bedtimes and eating habits), drinking habits, smoking habits, and diabetes medications, were obtained from participants’ medical records and through a self-reported general questionnaire. Body composition was measured using the bioelectrical impedance method (HBF-375; OMRON Healthcare Co., Ltd., Kyoto, Japan).

Diabetes-related factors

The duration, treatment, and complications of diabetes and blood test results were obtained from participants’ medical records. Hypoglycemia was confirmed by reviewing self-monitored blood glucose records or hypoglycemic episodes reported via a self-reported questionnaire during the last 3 months. Hypoglycemia was defined as a blood glucose level < 70 mg/dL [24] or the presence of hypoglycemic symptoms that improved with carbohydrate intake. Diabetes self-management performance was measured using the Summary of Diabetes Self-Care Activities Measure (SDSCA) [25]. The higher the subscale mean score, the higher the level of self-care practice.

Frailty

Frailty was evaluated using the Kihon Checklist (KCL), which was developed and validated by the Japanese Ministry of Health, Labor, and Welfare and is widely used in Japan [26]. The KCL has been translated into other languages and used in various countries [27]. This comprehensive questionnaire assesses multiple domains, including the physical, psychological, functional, and social statuses of older adults without disabilities. A higher score in each KCL domain indicates a higher risk of requiring support or care. KCL scores of ≥ 8 and ≥ 4 points were defined as frailty and pre-frailty, respectively [26].

Physical functions

Grip strength was measured using a digital hand dynamometer (T.K.K.5401; Takei Scientific Instruments Co., Ltd., Niigata Prefecture, Japan). The participants were assessed upright, holding the grip dynamometer such that the second joint of the index finger was at 90°.

Statistical analysis

The participants were categorized into urban and rural areas based on the location of their hospitals to examine regional differences in frailty. Based on the criteria of the Ministry of Internal Affairs and Communications in Japan, urban areas were defined as regions included in the 23 special wards of Tokyo, the government ordinance-designated cities, the municipalities that are contiguous to these areas, and where at least 1.5% of residents aged ≥ 15 years commute to work or school in these areas [28]. Therefore, an urban area was defined as a central city with a population of > 1 million and surrounding municipalities that commute to the central city for work or school.

Data were expressed as mean ± standard deviation for continuous variables or number (percentage) for categorical variables unless otherwise noted. Student’s t-test was used to compare continuous variables, whereas the chi-square and Fisher’s exact tests were used to compare categorical variables according to region.

Logistic regression analyses were performed to evaluate the relationship between frailty and region, adjusting for demographic factors (age and gender) and the basic background of people with diabetes (Hemoglobin A1c [HbA1c] level and diabetes duration).

Statistical significance was set at p < 0.05. Data analyses were performed using SPSS statistics (version 28.0; IBM, Japan).

Results

Participants’ characteristics

Among 421 eligible participants, four were excluded because of certification for long-term care needs (n = 2), withdrawal of consent (n = 1), or incomplete data (n = 1). Finally, 417 participants (269 urban [64.5%] and 148 rural [35.5%]) were enrolled and analyzed (Table 1).

Table 1 Participants’ demographics
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The mean ages of the participants in urban and rural areas were 70.6 ± 5.5 and 69.0 ± 5.2 years, respectively. In addition, the proportion of women, mean body mass index, diabetes duration, HbA1c level, serum albumin level, and low-density lipoprotein cholesterol level in urban and rural areas were 48.0% and 43.2%, 24.1 ± 3.6 and 25.0 ± 4.0 kg/m2, 16.6 ± 10.9 and 12.0 ± 10.3 years, 7.3 ± 1.0% (57 ± 11 mmol/mol) and 7.0 ± 0.9% (53 ± 10 mmol/mol), 4.16 ± 0.31 and 4.30 ± 0.38 g/ml, and 103.7 ± 28.7 and 106.2 ± 26.6 mg/dl, respectively.

The proportion of insulin-treated older adults and the diet- and exercise-related SDSCA scores were higher in urban areas than in rural areas (p = 0.001, 0.003, and 0.036, respectively). The medication-related SDSCA score was significantly higher in rural areas than in urban areas (p = 0.001). Regarding diabetic complications, the prevalence of hypoglycemia, serious hypoglycemia, nephropathy, peripheral neuropathy, and peripheral artery disease was significantly higher in urban areas than in rural areas.

Among lifestyle-related factors, only the smoking habit rate was higher in rural areas than in urban areas (p = 0.005). Drinking habit rate, employment rate, education level, the proportion of people living alone, and irregular lifestyle were not significantly different between the regions.

Prevalence and characteristics of frailty

Regional differences were observed in the prevalence of frailty, with robustness being particularly significantly lower in rural areas than in urban areas (29.7% vs. 43.9%, p = 0.018). The scores on the instrumental ADL (IADL) and social ADL (SADL) subscales of KCL were significantly higher in rural areas (p < 0.001, p = 0.029). Grip strength was also higher in rural areas (p = 0.001). No significant differences existed between the total and other subscale scores for KCL and body composition (Table 2).

Table 2 Body composition and frailty
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Relationship between frailty and region

The logistic regression analyses showed that frailty was significantly associated with the region (odds ratio [OR] = 2.55, 95% confidence interval [CI]: 1.38–4.71, p = 0.003) and HbA1c levels (OR = 1.45, 95% CI: 1.10–1.93, p = 0.010) after adjusting for age, gender, HbA1c levels, and diabetes duration. Pre-frailty was significantly associated with the region (OR = 2.10, 95% CI: 1.30–3.41, p = 0.003) (Table 3).

Table 3 Relationship between frailty and region
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Relationship between subscale score of KCL and region

After adjusting for age, gender, HbA1c levels, and diabetes duration in the multiple linear regression analysis, regional differences were found in IADL and SADL but not in physical activities, nutritional status, oral function, cognitive function, and depressive mood (Table 4).

Table 4 Relationship between subscale scores of KCL and region
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The IADL and SADL scores were significantly associated with region (rural areas) (B = 0.279, standard error [SE] = 0.073, p < 0.001; B = 0.265, SE = 0.097, p = 0.006) and gender (women) (B = − 0.437, SE = 0.067, p < 0.001; B = -0.211, SE = 0.090, p = 0.020), respectively.

Physical activity scores were significantly associated with age (B = 0.024, SE = 0.011, p = 0.025), gender (women) (B = 0.668, SE = 0.112, p < 0.001), and HbA1c levels (B = 0.172, SE = 0.059, p = 0.004). Nutritional status scores were significantly associated with age (B = -0.015, SE = 0.005, p < 0.001). Oral and cognitive function scores were significantly associated with gender (women) (B = 0.308, SE = 0.087, p < 0.001; B = 0.151, SE = 0.073, p = 0.039). Depressive mood was significantly associated with gender (women) (B = 0.371, SE = 0.115, p = 0.001) and HbA1c levels (B = 0.161, SE = 0.061, p = 0.009).

For reference, the responses to each question on the KCL were compared between the urban and rural areas (Table 5). Of 25 questions assessing frailty, implementation was significantly higher in urban areas than in rural areas for four items: “1. Do you go out by bus or train by yourself?” (90.7% vs. 67.6%, p < 0.001), “4. Do you sometimes visit your friends?” (68.4% vs. 57.4%, p = 0.025), “5. Do you turn to your family or friends for advice?” (84.0% vs. 75.7%, p = 0.038), and “8. Do you normally walk continuously for 15 minutes?” (90.7% vs. 82.4%, p = 0.014).

Table 5 Percentage of “yes” responses to each question on KCL
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Discussion

The present study examined regional differences in frailty between urban and rural areas among older adults with diabetes who could independently perform basic ADLs. Results showed that living in rural areas was associated with frailty and pre-frailty. Frailty in rural areas was characterized by lower IADL and SADL scores. Lower IADL and SADL scores were also associated with gender (men) but not with age, diabetes duration, or HbA1c levels.

IADLs include activities necessary for social life, such as shopping, making phone calls, and using public transportation, and complex ADLs, such as washing clothes and cooking. SADLs include interactions with others and going out. The lower IADL and SADL scores in rural areas suggest a higher risk of social frailty in rural areas than in urban areas. The risk of mortality owing to social frailty was reported to be 2.69 times higher than that towing to physical frailty [29, 30]. Social frailty precedes and causes impaired cognitive, physical, and psychological functioning [31, 32]. Therefore, it is important to prevent social frailty in rural areas.

In contrast, age, diabetes duration, and HbA1c levels were not associated with a decline in IADLs and SADLs. HbA1c levels and diabetes duration have generally been associated with frailty in older adults with diabetes [19, 33, 34], although some studies have found no association [35]. Because a previous study showed a stronger association between HbA1c and prognosis in individuals with physical frailty [36], the association with social ADLs in individuals with independent ADLs, such as the participants in this study, was considered weak. Therefore, glycemic control alone may be insufficient to prevent frailty in rural areas. In addition, the participants in the present study regularly attended diabetes outpatient clinics. Therefore, healthcare professionals in rural areas need to know the risk of decreased social activity and assess and promote such activity, even among older adults with diabetes who independently perform ADLs and can maintain hospital visits.

Notably, many participants in rural areas did not use public transportation or walk for > 15 min. This indicates that cars are their primary means of transportation and that they have few opportunities to walk unless they are aware of them. Furthermore, many did not visit friends or advise family or friends. Therefore, they may have fewer opportunities to interact deeply with others and be more isolated.

The availability of facilities and services related to necessary daily living functions and social participation within walking and biking distances are considered related to the social activities of older adults [37, 38]. In rural areas, accessibility to train stations, stores, bank automated teller machines, community centers, parks, and other social amenities that facilitate outings (safe sidewalks, outdoor lights, restrooms, and benches) is likely lacking. Therefore, lifestyle interventions to overcome this environmental disadvantage should be included in diabetes self-management education programs.

Because hospital visits are an important opportunity to prevent frailty in older adults with diabetes, it is necessary to support them in safely increasing their activity by suggesting that they park in a remote parking lot to increase their walking distance, assess peripheral neuropathy as a risk factor for frailty, and suggest an appropriate exercise load based on an assessment of diabetic complications. Furthermore, peer support programs that involve extensive interaction with others and group sessions that allow people to experience exercise and diet therapy while making friends need to be actively incorporated into diabetes education programs for older adults.

In addition, one possible factor associated with the higher risk of frailty in rural areas may be differences in health literacy. Many reports associate health literacy with frailty, indicating that older adults who have difficulty acquiring information, making decisions, and taking action in various health-related daily life situations are more likely to become frail in the future [39,40,41,42]. Low health literacy is also associated with poor self-management ability [43], non-adherence to treatment [44], low use of preventive services such as cancer screening [45], higher hospital readmission rates [46], and an increased risk of overall mortality [47]. Participants in both areas were regularly examined by diabetologists and received support from certified diabetes educators (CDEs), certified nurse practitioners, and nutritionists, indicating no significant differences in diabetes care or patient education between the rural and urban areas. However, the rate of implementation of health behaviors, such as diet, exercise, and smoking cessation, was higher in urban areas than in rural areas. Since a systematic review comparing health literacy in urban and rural areas also reported higher health literacy in urban areas [48], it will be necessary to consider local health literacy levels in frailty prevention.

Given these regional differences, intervention programs that combine individualized risk-based frailty prevention, diabetes management, and online health communication strategies delivered across geographic and time constraints are necessary. Having a smartphone improves health literacy and social support in older adults, lowering the risk of frailty [49]. While aging is associated with lower health literacy [50], a large longitudinal cohort study in the United Kingdom reported that internet use and social engagement help maintain health literacy maintenance in older adults [51]. Therefore, improved health literacy and internet use are key elements in both diabetes self-care and frailty prevention in older adults with diabetes. There is currently little evidence of the effectiveness of information and communication technologies in preventing frailty in older adults; however, a previous report found that a web-delivered group exercise intervention improved physical function in older adults with type 2 diabetes [52]. Improvements in HbA1c levels, patient activation, and self-efficacy were reported in people with type 2 diabetes who participated in an online diabetes self-management program compared with a usual care control group [53]. Based on previously identified risk factors [19], establishing an online program that assesses the individual risk of frailty, suggests risk-based frailty prevention strategies, and provides support may be useful.

This study had some limitations. First, the self-reported survey items may have introduced some reporting bias. However, this effect was minimized because the assessment instruments were reliable and validated, and most items were administered by trained nurses responsible for collecting measurements and information from the medical records. Second, there is no precise information on whether participants have lived in each region their entire lives. However, most participants have attended the same hospital or clinic for several years to a decade or more. Therefore, it is likely that few have moved to widely different environments, such as rural and urban areas, at least during the older age period. Additionally, not all participants underwent routine evaluation for hypoglycemia, including blood glucose or continuous glucose monitoring, which may have led to overlooking asymptomatic hypoglycemia. Specifically, self-monitoring of blood glucose was mainly performed by patients treated with insulin or GLP-1 RA, as continuous glucose monitoring was uncommon. Consequently, hypoglycemia may have been overlooked in patients treated with oral therapy. However, less severe hypoglycemia can be confirmed by patient self-reporting [24, 54]. Therefore, in this study, all information related to hypoglycemia was obtained, and information bias was minimized by evaluating both blood glucose measurement and self-report findings. Finally, although this was a nationwide survey, not all areas were covered. Notably, areas without diabetologists or diabetes-specific medical staff were not surveyed. Further research covering areas with poor access to specialized medical services is required.

Conclusions

The results of this study indicate that older adults with diabetes who can independently perform ADLs are at a higher risk of frailty in rural areas, as characterized by a decline in IADLs and SADLs. The social environment should be considered in risk assessments for frailty prevention. In addition to environmental adjustments based on individual risks, intervention programs should be designed to include communication strategies that enable care and social participation across different environments.

Data availability

The datasets used and/or analysed in this study are available from the corresponding authors upon reasonable request and with the approval of the Ethics Committee for the transfer of data.

References

  1. Morley JE, Vellas B, van Kan GA, Anker SD, Bauer JM, Bernabei R, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14(6):392–7.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sezgin D, O’Donovan M, Cornally N, Liew A, O’Caoimh R. Defining frailty for healthcare practice and research: a qualitative systematic review with thematic analysis. Int J Nurs Stud. 2019;92:16–26.

    Article  PubMed  Google Scholar 

  3. Vermeiren S, Vella-Azzopardi R, Beckwee D, Habbig AK, Scafoglieri A, Jansen B, et al. Frailty and the prediction of negative health outcomes: a meta-analysis. J Am Med Dir Assoc. 2016;17(12):1163.e1–e17.

    Article  PubMed  Google Scholar 

  4. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Cardiovascular Health Study Collaborative Research Group, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–56.

    Article  CAS  Google Scholar 

  5. Dent E, Morley JE, Cruz-Jentoft AJ, Woodhouse L, Rodríguez-Mañas L, Fried LP, et al. Physical frailty: ICFSR international clinical practice guidelines for identification and management. J Nutr Health Aging. 2019;23(9):771–87.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Lang PO, Michel JP, Zekry D. Frailty syndrome: a transitional state in a dynamic process. Gerontology. 2009;55(5):539–49.

    Article  PubMed  Google Scholar 

  7. Sezgin D, O’Donovan M, Woo J, Bandeen-Roche K, Liotta G, Fairhall N, et al. Early identification of frailty: developing an international delphi consensus on pre-frailty. Arch Gerontol Geriatr. 2022;99:104586.

    Article  PubMed  Google Scholar 

  8. Dent E, Lien C, Lim WS, Wong WC, Wong CH, Ng TP, Woo J, et al. The Asia-Pacific clinical practice guidelines for the management of frailty. J Am Med Dir Assoc. 2017;18(7):564–75.

    Article  PubMed  Google Scholar 

  9. García-Esquinas E, Graciani A, Guallar-Castillón P, López-García E, Rodríguez-Mañas L, Rodríguez-Artalejo F. Diabetes and risk of frailty and its potential mechanisms: a prospective cohort study of older adults. J Am Med Dir Assoc. 2015;16:748–84.

    Article  PubMed  Google Scholar 

  10. Hanlon P, Fauré I, Corcoran N, Butterly E, Lewsey J, McAllister D, et al. Frailty measurement, prevalence, incidence, and clinical implications in people with diabetes: a systematic review and study-level meta-analysis. Lancet Healthy Longev. 2020;1(3):e106–16.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Yoon SJ, Kim KI. Frailty and disability in diabetes. Ann Geriatr Med Res. 2019;23(4):165–9.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chao CT, Wang J, Chien KL. COhort of GEriatric nephrology in NTUH (COGENT) study group. Both pre-frailty and frailty increase healthcare utilization and adverse health outcomes in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2018;17(1):130.

    Article  PubMed  PubMed Central  Google Scholar 

  13. O’Donovan M, Sezgin D, O’Caoimh R, Liew A. The impact of and interaction between diabetes and frailty on psychosocial wellbeing and mortality in Ireland. Int J Environ Res Public Health. 2020;17(24):9535.

    Article  PubMed  PubMed Central  Google Scholar 

  14. O’Donovan M, Sezgin D, O’Caoimh R, Liew A. The relationship between frailty and diabetes: an investigation of self-rated health, depression symptoms and quality of life in the study of health aging and retirement in Europe. Arch Gerontol Geriatr. 2021;96:104448.

    Article  PubMed  Google Scholar 

  15. Feng Z, Lugtenberg M, Franse C, Fang X, Hu S, Jin C, et al. Risk factors and protective factors associated with incident or increase of frailty among community-dwelling older adults: a systematic review of longitudinal studies. PLoS ONE. 2017;12(6):e0178383.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Cardiovascular health study collaborative research group. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–156.

    Article  CAS  Google Scholar 

  17. Cheong CY, Nyunt MSZ, Gao Q, Gwee X, Choo RWM, Yap KB, et al. Risk factors of progression to frailty: findings from the Singapore longitudinal ageing study. J Nutr Health Aging. 2020;24(1):98–106.

    Article  PubMed  CAS  Google Scholar 

  18. O’Caoimh R, Sezgin D, O’Donovan MR, Molloy DW, Clegg A, Rockwood K, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. 2021;50(1):96–104.

    Article  PubMed  Google Scholar 

  19. Nishimura A, Harashima SI, Hosoda K, Arai H, Inagaki N. Sex-related differences in frailty factors in older persons with type 2 diabetes: a cross-sectional study. Ther Adv Endocrinol Metab. 2019;10:2042018819833304.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Strain WD, Down S, Brown P, Puttanna A, Sinclair A. Diabetes and frailty: an expert consensus statement on the management of older adults with type 2 diabetes. Diabetes Ther. 2021;12(5):1227–47.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Nishimura A, Masuda C, Murauchi C, Ishii M, Murata Y, Kawasaki T et al. Relationship between frailty and diabetic pharmacologic therapy in older adults with type 2 diabetes: a cross-sectional study. Drugs Aging. 2024;41(6):531–542.

  22. Huang CY, Lee WJ, Lin HP, Chen RC, Lin CH, Peng LN, et al. Epidemiology of frailty and associated factors among older adults living in rural communities in Taiwan. Arch Gerontol Geriatr. 2020;87:103986.

    Article  PubMed  Google Scholar 

  23. Xu R, Li Q, Guo F, Zhao M, Zhang L. Prevalence and risk factors of frailty among people in rural areas: a systematic review and meta-analysis. BMJ Open. 2021;11(4):e043494.

    Article  PubMed  PubMed Central  Google Scholar 

  24. American Diabetes Association Professional Practice Committee. 6. Glycemic goals and hypoglycemia: standards of care in diabetes—2024. Diabetes Care 2024; 47(Suppl 1):S111–S125.

  25. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23:943–50.

    Article  PubMed  CAS  Google Scholar 

  26. Sewo Sampaio PY, Sampaio RA, Yamada M, Arai H. Systematic review of the Kihon checklist: is it a reliable assessment of frailty? Geriatr Gerontol Int. 2016;16:893–902.

    Article  PubMed  Google Scholar 

  27. Arai H, Satake S. English translation of the Kihon Checklist. Geriatr Gerontol Int. 2015;15:518–9.

    Article  PubMed  Google Scholar 

  28. Ministry of Internal Affairs and Communications. Explanation of regional classifications used in statistical tables. Accessed 24. December 2023. https://www.stat.go.jp/data/kokusei/2000/guide/2-01.htm.

  29. Teo N, Gao Q, Nyunt MSZ, Wee SL, Ng TP. Social Frailty and functional disability: findings from the Singapore longitudinal ageing studies. J Am Med Dir Assoc. 2017;18(7):673. .e13–637.e19.

    Article  Google Scholar 

  30. Garre-Olmo J, Calvó-Perxas L, López-Pousa S, de Gracia Blanco M, Vilalta-Franch J. Prevalence of frailty phenotypes and risk of mortality in a community-dwelling elderly cohort. Age Aging. 2013;42(1):46–51.

    Article  Google Scholar 

  31. Tsutsumimoto K, Doi T, Makizako H, Hotta R, Nakakubo S, Makino K, et al. Association of social frailty with both cognitive and physical deficits among older people. J Am Med Dis Assoc. 2017;18(7):603–7.

    Article  Google Scholar 

  32. Makizako H, Shimada H, Doi T, Tsutsumimoto K, Hotta R, Nakakubo S, et al. Social frailty leads to the development of physical frailty among physically non-frail adults: a four-year follow-up longitudinal cohort study. Int J Environ Res Public Health. 2018;15(3):490.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zaslavsky O, Walker RL, Crane PK, Gray SL, Larson EB. Glucose levels and risk of frailty. J Gerontol Biol Sci Med Sci. 2016;71:1223–9.

    Article  CAS  Google Scholar 

  34. Yanagita I, Fujihara Y, Eda T, Tajima M, Yonemura K, Kawajiri T, et al. Low glycated hemoglobin level is associated with severity of frailty in Japanese elderly diabetes patients. J Diabetes Investig. 2018;9(2):419–25.

    Article  PubMed  CAS  Google Scholar 

  35. Aguayo GA, Fagherazzi G. Intricate relationships between frailty and diabetes: where do we go from here? Lancet Healthy Longev. 2020;1(3):e92–3.

    Article  PubMed  Google Scholar 

  36. Hanlon P, Jani BD, Butterly E, Nicholl B, Lewsey J, McAllister D, et al. An analysis of frailty and multimorbidity in 20,566 UK biobank participants with type 2 diabetes. Commun Med. 2021;1:28.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Adorno G, Fields N, Cronley C, Parekh R, Magruder K. Ageing in a low-density urban city: transportation mobility as a social equity issue. Ageing Soc. 2018;38(2):296–320.

    Article  Google Scholar 

  38. Building Research Institute, National Research and Development, Agency. Japan. A handbook for community building to promote elderly people’s participation in community activities. Building Res Data 2016;178.

  39. Huang CH, Lai YC, Lee YC, Teong XT, Kuzuya M, Kuo KM. Impact of health literacy on frailty among community-dwelling seniors. J Clin Med. 2018;7(12):481.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yoshizawa Y, Tanaka T, Takahashi K, Fujisaki-Sueda-Sakai M, Son BK, Iijima K. Impact of health literacy on the progression of frailty after 4 years among community-dwelling older adults. Int J Environ Res Public Health. 2021;19(1):394.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Uemura K, Tsukasa K, Watanabe A, Okamoto H, Yamada M. Association between community-level health literacy and frailty in community-dwelling older adults. Aging Clin Exp Res. 2023;35(6):1253–61.

    Article  PubMed  Google Scholar 

  42. Choi EY, Shin H, Kim S, Lee HY, Kim YS. Limited health literacy increases the risk of frailty among community-dwelling older adults: longitudinal findings from the Korean Frailty and Aging Cohort Study. Geriatr Gerontol Int. 2022;22(4):325–31.

    Article  PubMed  Google Scholar 

  43. Geboers B, de Winter AF, Spoorenberg SL, Wynia K, Reijneveld SA. The association between health literacy and self-management abilities in adults aged 75 and older, and its moderators. Qual Life Res. 2016;25(11):2869–77.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Miller TA. Health literacy and adherence to medical treatment in chronic and acute illness: a meta-analysis. Patient Educ Couns. 2016;99(7):1079–86.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kobayashi LC, Wardle J, von Wagner C. Limited health literacy is a barrier to colorectal cancer screening in England: evidence from the English Longitudinal Study of Ageing. Prev Med. 2014;61(100):100–5.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun. 2012;17(Suppl 3):S325–338.

    Article  Google Scholar 

  47. Bostock S, Steptoe A. Association between low functional health literacy and mortality in older adults: longitudinal cohort study. BMJ. 2012;344:e1602.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Aljassim N, Ostini R. Health literacy in rural and urban populations: a systematic review. Patient Educ Couns. 2020;103(10):2142–54.

    Article  PubMed  Google Scholar 

  49. Yi J, Yoon JY, Won CW, Kim M, Lee KS. The roles of health literacy and social support in the association between smartphone ownership and frailty in older adults: a moderated mediation model. BMC Public Health. 2024;24(1):106.

    Article  Google Scholar 

  50. Baker DW, Gazmararian JA, Sudano J, Patterson M. The association between age and health literacy among elderly persons. J Gerontol B Psychol Sci Soc Sci. 2000;55(6):368–74.

    Article  Google Scholar 

  51. Kobayashi LC, Wardle J, von Wagner C. Internet use, social engagement and health literacy decline during ageing in a longitudinal cohort of older English adults. J Epidemiol Community Health. 2015;69(3):278–83.

    Article  PubMed  Google Scholar 

  52. Kirwan M, Chiu CL, Laing T, Chowdhury N, Gwynne K. A web-delivered, clinician-led group exercise intervention for older adults with type 2 diabetes: single-arm pre-post intervention. J Med Internet Res. 2022;24(9):e39800.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Lorig K, Ritter PL, Laurent DD, Plant K, Green M, Jernigan VBB, Case S. Online diabetes self-management program: a randomized study. Diabetes Care. 2010;33:1275–81.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Au NH, Ratzki-Leewing A, Zou G, Ryan BL, Webster-Bogaert S, Reichert SM, et al. Real-world incidence and risk factors for daytime and nocturnal non-severe hypoglycemia in adults with type 2 diabetes mellitus on insulin and/or secretagogues (InHypo-DM Study, Canada). Can J Diabetes. 2022;46:196–e2032.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank the participants for participating in this study.

Funding

This work was supported by JSPS KAKENHI (Grant Number JP 20H03983).

The study sponsor/funder was not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.

Author information

Authors and Affiliations

  1. Department of Chronic Care Nursing, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan

    Akiko Nishimura, Mayumi Azuma & Shin-ichi Harashima

  2. Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto City, Kyoto, 606-8507, Japan

    Akiko Nishimura, Mayumi Azuma & Shin-ichi Harashima

  3. Department of Internal Medicine and Diabetes, Goshominami Harashima Clinic, 630 Heinouchi-cho, Nakagyo-ku, Kyoto City, Kyoto, 604-0884, Japan

    Akiko Nishimura & Shin-ichi Harashima

  4. Department of Nursing, Asahikawa City Hospital, 1-1-65 Kinsei-cho, Asahikawa City, Hokkaido, 070-8610, Japan

    Chie Masuda

  5. Faculty of Nursing and Graduate School of Nursing, Kansai Medical University, 2-3-1 Shinmachi, Hirakata City, Osaka, 573-1010, Japan

    Chiyo Murauchi

  6. Jonan branch, Town Home-visit Medical Care Clinic, Gardenia Kamiikedai 101, 1-40-6 Kamiikedai, Ota-ku, Tokyo, 145-0064, Japan

    Miho Ishii

  7. Department of Nursing, Takashima Municipal Hospital, 1667 Katsuno, Takashima City, Shiga, 520-1121, Japan

    Yuko Murata

  8. Department of Nursing, Sapporo City General Hospital, 1-1 Kita, 11-jo Nishi, 13-chome, Chuo- ku, Sapporo City, Hokkaido, 064-0064, Japan

    Terumi Kawasaki

  9. Department of Internal Medicine, Miyazaki Prefectural Miyazaki Hospital, 5-30 Kitatakamatsu- cho, Miyazaki City, Miyazaki, 880-8510, Japan

    Mayumi Azuma

  10. National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City, Aichi, 474-8511, Japan

    Hidenori Arai

  11. Clinical Research Planning and Administration Division, National Hospital Organization, Kyoto Medical Center, 1-1 Fukakusa Mukabatake-cho, Fushimi-ku, Kyoto City, Kyoto, 611-8555, Japan

    Shin-ichi Harashima

  12. Research Center for Healthcare, Nagahama City Hospital, 313 Ohinui-cho, Nagahama City, Shiga, 526-8580, Japan

    Shin-ichi Harashima

Authors
  1. Akiko Nishimura
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  2. Chie Masuda
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  3. Chiyo Murauchi
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  4. Miho Ishii
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  5. Yuko Murata
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  6. Terumi Kawasaki
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  7. Mayumi Azuma
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  8. Hidenori Arai
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  9. Shin-ichi Harashima
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Contributions

All authors were involved in the study’s conception, design, and conduct, as well as the analysis and interpretation of the results. A.N. wrote the first draft of the manuscript. All authors edited, reviewed, and approved the final version of the manuscript. S.H. is the guarantor of this work, has full access to all the data in the study, and takes responsibility for the integrity of the data and accuracy of the data analysis.

Corresponding authors

Correspondence to Akiko Nishimura or Shin-ichi Harashima.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Kyoto University Graduate School and Faculty of Medicine Ethics Committee (R1373) and the ethics committee of each participating institution and was compliant with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their enrollment in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Nishimura, A., Masuda, C., Murauchi, C. et al. Regional differences in frailty among older adults with type 2 diabetes: a multicenter cross-sectional study in Japan. BMC Geriatr 24, 688 (2024). https://doi.org/10.1186/s12877-024-05223-7

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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