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Journal articles on the topic 'Fraility'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Rasheed, R., A. Patel, and Y. Shanthakumaran. "An observational study of the correlation of efi severity with depression." European Psychiatry 65, S1 (June 2022): S650—S651. http://dx.doi.org/10.1192/j.eurpsy.2022.1669.

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Introduction Patients with high fraility indices experience poor mental health due to multiple co morbidity and social isolation. Objectives This was a retrospective observational analysis that studied the correlation of Electronic frailty indices and GAD scores with Depression scores in a rural population. Methods An annual frailty assessment is offered to elderly patients and we screen routinely for anxiety and depression using the PHQ-9 score and GAD score. This was an observational study examining the correlation of the Electronic Frailty Indices (EFI) depression and anxiety scores. Results Of the 118 patients ranging from mild to severe frailty we found a positive correlation of the EFI with the Depression and anxiety scores. Within the data set, the correlation coefficient of EFI scores and PHQ 9 scores was found to be 0.819. Similarly within the same data set we found a correlation coefficient of EFI and GDS scores of 0.651. The higher the EFI the greater was the scale of dependency and comorbidity and this correlation was consistent across the data set with depression and anxiety. We believe physical impairment, loss of independence and social isolation cognitive decline contribute to loss of self-esteem. Conclusions Our study found a positive correlation between frailty severity based on EFI scores and depression and anxiety severity. Early detection in deterioration of mental health will enable supportive measures and targeted treatment strategies. Our study shows the strong correlation of EFI severity scores with worse mental health. Disclosure No significant relationships.

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Ogawa, Asao, Fumio Nagashima, and Tetsuya Hamaguchi. "Evaluation of the G8 screening tools for fraility in older patients with cancer." Annals of Oncology 26 (November 2015): vii119. http://dx.doi.org/10.1093/annonc/mdv472.59.

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3

Toolan, Shane, Marie Therese Cooney, and Orla Collins. "259ASSOCIATION BETWEEN FRAILITY, LENGTH OF STAY AND DISCHARGE DESTINATION IN THE ACUTE MEDICAL UNIT." Age and Ageing 45, suppl 2 (September 2016): ii13.127—ii56. http://dx.doi.org/10.1093/ageing/afw159.225.

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4

Kochar, Bharati, Winston Cai, Andrew Cagan, and Ashwin Ananthakrishnan. "Sa1830 FRAILITY IS INDEPENDENTLY ASSOCIATED WITH INCREASED MORTALITY IN PATIENTS WITH INFLAMMATORY BOWEL DISEASES." Gastroenterology 158, no. 6 (May 2020): S—443. http://dx.doi.org/10.1016/s0016-5085(20)31790-x.

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Monga, Shveta, Dominic Haigh, David Royan, Chitsa Seyani, Richard Francis, Paul Foley, and Badrinath Chandrasekaran. "19 Modified fraility index as predictor of outcome for patients implanted with cardiac resynchronisation therapy." Heart 103, Suppl 5 (June 2017): A15. http://dx.doi.org/10.1136/heartjnl-2017-311726.19.

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Kochar, Bharati, Winston Cai, Andrew Cagan, and Ashwin Ananthakrishnan. "281 FRAILITY IS ASSOCIATED WITH AN INCREASED RISK OF INFECTIONS IN INFLAMMATORY BOWEL DISEASE PATIENTS ON IMMUNOSUPPRESSION." Gastroenterology 158, no. 6 (May 2020): S—54. http://dx.doi.org/10.1016/s0016-5085(20)30818-0.

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7

Kim, Woo-Kyung, and Chung-Won Chung. "2014, 2017 National Survey of Older Koreans Frailty & Applying Frailty Measurement." Journal of Coaching Development 21, no. 3 (September 30, 2019): 101–10. http://dx.doi.org/10.47684/jcd.2019.09.21.3.101.

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8

Pierce, Katherine E., Peter G. Passias, Alan H. Daniels, Renaud Lafage, Waleed Ahmad, Sara Naessig, Virginie Lafage, et al. "Baseline Frailty Status Influences Recovery Patterns and Outcomes Following Alignment Correction of Cervical Deformity." Neurosurgery 88, no. 6 (February 20, 2021): 1121–27. http://dx.doi.org/10.1093/neuros/nyab039.

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Abstract BACKGROUND Frailty severity may be an important determinant for impaired recovery after cervical spine deformity (CD) corrective surgery. OBJECTIVE To evaluate postop clinical recovery among CD patients between frailty states undergoing primary procedures. METHODS Patients >18 yr old undergoing surgery for CD with health-related quality of life (HRQL) data at baseline, 3-mo, and 1-yr postoperative were identified. Patients were stratified by the modified CD frailty index scale from 0 to 1 (no frailty [NF] <0.3, mild/severe fraily [F] >0.3). Patients in NF and F groups were propensity score matched for TS-CL (T1 slope [TS] minus angle between the C2 inferior end plate and the C7 inferior end plate [CL]) to control for baseline deformity. Area under the curve was calculated for follow-up time intervals determining overall normalized, time-adjusted HRQL outcomes; Integrated Health State (IHS) was compared between NF and F groups. RESULTS A total of 106 CD patients were included (61.7 yr, 66% F, 27.7 kg/m2)—by frailty group: 52.8% NF, 47.2% F. After propensity score matching for TS-CL (mean: 38.1°), 38 patients remained in each of the NF and F groups. IHS-adjusted HRQL outcomes from baseline to 1 yr showed a significant difference in Euro-Qol 5 Dimension scores (NF: 1.02, F: 1.07, P = .016). No significant differences were found in the IHS Neck Disability Index (NDI) and modified Japanese Orthopedic Association between frailty groups (P > .05). F patients had more postop major complications (31.3%) compared to the NF (8.9%), P = .004, though DJK occurrence and reoperation between the groups was not significant. CONCLUSION While all groups exhibited improved postop disability and pain scores, frail patients experienced greater amount of improvement in overall health state compared to baseline disability. This signifies that with frailty severity, patients have more room for improvement postop compared to baseline quality of life.

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Ostroumova, Olga D., and Marina S. Cherniaeva. "Evidence base regarding target levels of arterial pressure in patients after a stroke: focus on a geriatric population." Systemic Hypertension 17, no. 1 (May 7, 2020): 51–61. http://dx.doi.org/10.26442/2075082x.2020.1.200039.

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Arterial hypertension (AH) is a very significant and most common risk factor for stroke, and lowering blood pressure (BP) is the most effective action to prevent stroke in patients with AH. This article provides an overview of existing randomized clinical trials (RCTs) and meta-analyzes to study the optimal target BP levels in patients with AH and cerebral events with a history of focusing on the geriatric population. As a result of the analysis of literature data, we obtained contradictory results: RCTs showed the benefits of lower target BP, in only two of them the target level of systolic BP (SBP) was less than 130 mm Hg, and the average age of patients included in the RCTs varied from 60 to 68 years, the number of patients older than 75 years was insignificant, and some studies excluded patients older than 85 years. Several subanalyses analyzed by RCTs found a J-shaped relationship between BP levels achieved and the risk of adverse cardiovascular events. One of them showed that with a decrease in BP below the SBP 120 mm Hg and DBP 65 mm Hg higher BP was associated with a lower risk of cardiovascular events. Another one showed that the risk of re-stroke and the risk of subsequent adverse events was statistically significantly higher in patients with an average level of SBP below 120 mm Hg than in patients with a level of SBP of 130139 mm Hg. The meta-analysis published in the Cochrane database did not show significant advantages of lowering BP130/85 mm Hg versus standard lowering BP140160/90100 mm Hg, while another meta-analysis revealed some advantages of a more intense decrease in BP for the prevention of re-stroke. Existing studies did not take into account the heterogeneity of the geriatric population and did not include patients with fraility and multimorbidity patients, and did not take into account the type of stroke to develop a differentiated approach to the tactics of lowering BP in patients of all age groups. Therefore, in relation to the recommended ESC/ESH experts in 2018, target BP levels of 120129/7079 mm Hg (aged 1865 years) and 130139/7079 mmHg (aged 65 years) for patients with AH and a history of cerebral events, there are still some doubts about the benefits of lower target BP values for the prevention of re-stroke and other cardiovascular events for the entire population of geriatric patients, especially patients older than 75, so specially designed randomized controlled trials are needed.

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Cusack, Britt, and Christian Harbin. "“Frailing” or Failing? Defining frailty syndrome vs adult failure to thrive for clinical practice." Geriatric Nursing 38, no. 5 (September 2017): 464–65. http://dx.doi.org/10.1016/j.gerinurse.2017.08.010.

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Monacelli, Fiammetta, Alessio Signori, Matteo Prefumo, Chiara Giannotti, Alessio Nencioni, Emanuele Romairone, Stefano Scabini, and Patrizio Odetti. "Delirium, Frailty, and Fast-Track Surgery in Oncogeriatrics: Is There a Link?" Dementia and Geriatric Cognitive Disorders Extra 8, no. 1 (February 7, 2018): 33–41. http://dx.doi.org/10.1159/000486519.

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Background/Aims: Postoperative delirium (POD) is more frequent in elderly patients undergoing major cancer surgery. The interplay between individual clinical vulnerability and a series of perioperative factors seems to play a relevant role. Surgery is the first-line treatment option for cancer, and fast-track surgery (FTS) has been documented to decrease postoperative complications. The study sought to assess, after comprehensive geriatric assessment (CGA) and frailty stratification (Rockwood 40 items index), which perioperative parameters were predictive of POD development in elderly patients undergoing FTS for colorectal cancer. Methods: A total of 107 consecutive subjects admitted for elective colorectal FTS were enrolled. All patients underwent CGA, frailly stratification, Timed up & go (TUG) test, 4AT test for delirium screening, anesthesiologists physical status classification, and Dindo-Clavien classification. Results: The incidence of POD was 12.3%. Patients’ prevalent clinical phenotype was pre-frail. The multivariate analysis indicated physical performance (TUG in seconds) as the most significant predictor of POD for each second of increase. Conclusions: Only few procedure-specific studies have examined the impact of FTS for colorectal cancer on POD. This is the first study to investigate the risk factors for POD, in a vulnerable octogenarian oncogeriatric population submitted to FTS surgery and frailty stratification.

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12

Hayashi, Yuichi. "Frailty." Nippon Shokuhin Kagaku Kogaku Kaishi 64, no. 8 (2017): 446. http://dx.doi.org/10.3136/nskkk.64.446.

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13

Zhao, Pei Jun, and Stefan Rodic. "Frailty." University of Western Ontario Medical Journal 84, no. 2 (November 9, 2015): 12–13. http://dx.doi.org/10.5206/uwomj.v84i2.4281.

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14

Gorman, Thomas James. "Frailty." InnovAiT: Education and inspiration for general practice 8, no. 9 (July 28, 2015): 547–54. http://dx.doi.org/10.1177/1755738015595825.

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Addison, Odessa. "FRAILTY." Innovation in Aging 3, Supplement_1 (November 2019): S574. http://dx.doi.org/10.1093/geroni/igz038.2128.

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Abstract The ability to safely maintain mobility function with aging is critical as immobility and falls are among the top reasons for long-term care admissions. One potential cause for these functional deficits are muscle composition changes resulting in reductions in muscle mass, strength and power, ultimately contributing to the development of frailty. While the majority of work examining muscle composition and mobility changes with aging have focused on the quadriceps and ankle plantarflexor/dorsiflexor muscles, accumulating evidence suggests that deficits involving the proximal hip muscles may be particularly harmful to balance and mobility functions leading to falls, hip fractures, and frailty. We will discuss muscle changes that occur with aging and frailty, the implications on mobility, and the effects of potential exercise interventions on muscle structure and function as well as their ability to improve functional mobility.

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Tallman, Dina, Deepinder Kaur, and Pramod Khosla. "Frailty." Journal of Renal Nutrition and Metabolism 3, no. 1 (2018): 13. http://dx.doi.org/10.4103/2395-1540.232543.

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17

Zaslavsky, Oleg, Barbara B. Cochrane, Hilaire J. Thompson, Nancy F. Woods, Jerald R. Herting, and Andrea LaCroix. "Frailty." Biological Research For Nursing 15, no. 4 (October 18, 2012): 422–32. http://dx.doi.org/10.1177/1099800412462866.

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Frailty is an emerging geriatric syndrome that refers to a state of increased vulnerability to adverse events including mortality, morbidity, disability, hospitalization, and nursing home admission. Despite its long conceptual and operational history in research and publications, frailty and mechanisms of frailty development are still poorly understood. In this review, we describe a number of conceptual models—reliability, allostatic load, and complexity—that have been put forward to explain the dynamic nature of frailty. We illustrate a consolidated pathophysiological model of frailty, taking into consideration the large and exponentially growing body of studies regarding predictors, indicators, and outcomes of frailty. The model addresses cellular (e.g., oxidative damage and telomere length) and systemic mechanisms (e.g., endocrinal, inflammatory, coagulatory, and metabolic deficiencies) of frailty, moderating or risk factors (e.g., ethnicity, lifestyle, and comorbidities), and outcomes (morbidity, disability, and cognitive decline). Finally, we identify the weaknesses of traditional epidemiological approaches for studying complex phenomena related to frailty and propose areas for future methodological and physiological inquiry.

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18

Wakabayashi, Hidetaka. "Frailty." Nihon Naika Gakkai Zasshi 108, no. 2 (February 10, 2019): 258–63. http://dx.doi.org/10.2169/naika.108.258.

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BROWN, I., R. RENWICK, and D. RAPHAEL. "Frailty." International Journal of Rehabilitation Research 18, no. 2 (June 1995): 93–102. http://dx.doi.org/10.1097/00004356-199506000-00001.

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Wang, C., X. Qiao, X. Tian, N. Liu, L. Dong, and R. L. Kane. "FRAILTY." Innovation in Aging 1, suppl_1 (June 30, 2017): 816–17. http://dx.doi.org/10.1093/geroni/igx004.2948.

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21

Bortz, Walter M. "Frailty." Mechanisms of Ageing and Development 129, no. 11 (November 2008): 680. http://dx.doi.org/10.1016/j.mad.2008.09.008.

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22

Morley, John E., Matthew T. Haren, Yves Rolland, and Moon Jong Kim. "Frailty." Medical Clinics of North America 90, no. 5 (September 2006): 837–47. http://dx.doi.org/10.1016/j.mcna.2006.05.019.

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23

von Renteln-Kruse, Wolfgang. "Frailty." Zeitschrift für Gerontologie und Geriatrie 49, no. 3 (April 2016): 262. http://dx.doi.org/10.1007/s00391-016-1055-8.

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24

Körtner, Ulrich H. J. "Frailty." Ethik in der Medizin 18, no. 2 (April 7, 2006): 108–19. http://dx.doi.org/10.1007/s00481-006-0419-9.

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25

Courtney-Brooks, Madeleine, A. Rauda Tellawi, Jennifer Scalici, Linda R. Duska, Amir A. Jazaeri, Susan C. Modesitt, and Leigh A. Cantrell. "Frailty." Obstetrical & Gynecological Survey 68, no. 1 (January 2013): 30–32. http://dx.doi.org/10.1097/01.ogx.0000426491.82300.a6.

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26

Cicutto, Lisa C. "Frailty." Chest 154, no. 1 (July 2018): 1–2. http://dx.doi.org/10.1016/j.chest.2018.03.041.

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Søndergaard, Lars, Bettina Højberg Kirk, and Troels Højsgaard Jørgensen. "Frailty." JACC: Cardiovascular Interventions 11, no. 4 (February 2018): 404–6. http://dx.doi.org/10.1016/j.jcin.2017.12.017.

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Kanamori, Takuya, Mizue Suzuki, Tomoyoshi Naito, Keigo Inagaki, and Hiroyuki Umegaki. "Frailty in a Frailty Prevention Program Participants During COVID-19 pandemic: A Cross-Sectional Japanese Study." Innovation in Aging 5, Supplement_1 (December 1, 2021): 816. http://dx.doi.org/10.1093/geroni/igab046.2999.

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Abstract Objective: Health conditions of older adults have deteriorated during the COVID-19 pandemic. Few studies have reported on the frailty of this group of people. The study aimed to investigate physical and social frailty in participants in a frailty prevention program during the COVID-19 pandemic. Methods: A cross-sectional survey was conducted in Japan from January 2021. Further, 863 participants of a frailty prevention program completed the survey. The frequency of program attendance in 2020, physical frailty (5-item frailty screening index), social frailty（diagnostic criteria of social frailty in NCGG-SGS), and depression (GDS-5) were assessed. A related factor of physical frailty was analyzed statistically by Welch's t test and the Chi-squared test. Results: The study participants’ mean age, proportion of women, and mean enrollment period in program were 86.8±4.9, 96.3%, 64.3±48.6 months, respectively. The program attendance ratio was 83.4% from January to March, 54.5% from April to June, 79.8% from July to September, and 80.0% from October to December. The prevalence of physical frailty was 20.3% non-frailty, 63.7% pre-frailty, and 15.6% frailty. The prevalence of social frailty was 10.0% non-frailty, 28.6% social pre-frailty, 61.8% social frailty, and the prevalence of depression was 36.8%. Participants with physical frailty were significantly older and showed higher prevalence of social frailty and depression, displaying significantly lower attendance program than non-frailty and pre-frailty older adults (p<0.05). Conclusions: The study results suggest that more than half of the participants of a frailty prevention program have social frailty and a high risk of physical frailty due to COVID-19.

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Komatsu, Ryo, Koutatsu Nagai, Yoko Hasegawa, Kazuki Okuda, Yuto Okinaka, Yosuke Wada, Shotaro Tsuji, et al. "Association between Physical Frailty Subdomains and Oral Frailty in Community-Dwelling Older Adults." International Journal of Environmental Research and Public Health 18, no. 6 (March 12, 2021): 2931. http://dx.doi.org/10.3390/ijerph18062931.

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This cross-sectional study aimed to demonstrate the association between physical frailty subdomains and oral frailty. This study involved community-dwelling older adults (aged ≥65 years). Physical frailty was assessed with the Japanese version of the Cardiovascular Health Study criteria. Oral frailty was defined as limitations in at least three of six domains. Logistic regression analysis was used to analyze the association between physical frailty risk and oral frailty. In addition, we examined the association between physical frailty subdomains (gait speed, grip strength, exhaustion, low physical activity, and weight loss) and oral frailty. A total of 380 participants were recruited for this study. Overall, 18% and 14% of the participants were at risk of physical frailty and had oral frailty, respectively. Physical frailty risk (odds ratio (OR) = 2.40, 95% confidence interval (CI): 1.22–4.75, p = 0.012) was associated with oral frailty in multivariate analysis. In secondary analysis, among physical frailty subdomains, gait speed (OR = 0.85, 95% CI: 0.73–0.97, p = 0.019) was associated with oral frailty. The present findings suggest that physical frailty is closely related to oral frailty. Among physical frailty subdomains, decreased gait speed in particular is an important indicator related to the development of oral frailty.

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MacEachern, Evan, Nicholas Giacomantonio, Olga Theou, Jack Quach, Wanda Firth, Ifedayo Abel-Adegbite, and Dustin Scott Kehler. "Comparing Virtual and Center-Based Cardiac Rehabilitation on Changes in Frailty." International Journal of Environmental Research and Public Health 20, no. 2 (January 14, 2023): 1554. http://dx.doi.org/10.3390/ijerph20021554.

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Many patients with cardiovascular disease (CVD) are frail. Center-based cardiac rehabilitation (CR) can improve frailty; however, whether virtual CR provides similar frailty improvements has not been examined. To answer this question, we (1) compared the effect of virtual and accelerated center-based CR on frailty and (2) determined if admission frailty affected frailty change and CVD biomarkers. The virtual and accelerated center-based CR programs provided exercise and education on nutrition, medication, exercise safety, and CVD. Frailty was measured with a 65-item frailty index. The primary outcome, frailty change, was analyzed with a two-way mixed ANOVA. Simple slopes analysis determined whether admission frailty affected frailty and CVD biomarker change by CR model type. Our results showed that admission frailty was higher in center-based versus virtual participants. However, we observed no main effect of CR model on frailty change. Results also revealed that participants who were frailer at CR admission observed greater frailty improvements and reductions in triglyceride and cholesterol levels when completing virtual versus accelerated center-based CR. Even though both program models did not change frailty, higher admission frailty was associated with greater frailty reductions and change to some CVD biomarkers in virtual CR.

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Kim, AlChan, Eunsurk Yi, Jiyoun Kim, and MunHee Kim. "A Study on the Influence of Social Leisure Activities on the Progression to the Stage of Frailty in Korean Seniors." International Journal of Environmental Research and Public Health 17, no. 23 (November 30, 2020): 8909. http://dx.doi.org/10.3390/ijerph17238909.

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In this study, we performed a logistic regression analysis according to the frequency of participation in social leisure activities (education, clubs, social groups, volunteer activities, religious activities, and senior citizens’ welfare center use) by men and women aged ≥ 65 years. We investigated the frequency of participation in social leisure activities and their association with the level of frailty (health vs. pre-frailty, health vs. frailty, pre-frailty vs. frailty). This study included 10,297 older adults (men: 4128, women: 6169) who participated in the 2017 National Survey of Older Koreans, and were divided into three groups (healthy, pre-frailty, and frailty). Five frailty index components were used to measure the frailty level. There was a positive relationship between the elderly’s religious activities, four times a week, from the healthy stage to the frailty stage, from the healthy stage to the pre-frailty stage, and from the pre-frailty stage to the frailty. In addition, positive associations emerged in leisure activities and club activities, respectively, from the healthy stage to the frailty stage (once a week, respectively). Positive association also emerged from the healthy stage to the pre-frailty and from the pre-frailty stage to the frailty stage (once a month to once in a two-week period).

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Gomez-Cabrero, D., S. Walter, I. Abugessaisa, L. Rodríguez-Mañas, and J. Tegner. "OMIC SIGNATURES IN FRAILTY AND FRAILTY DIAGNOSIS." Innovation in Aging 1, suppl_1 (June 30, 2017): 903–4. http://dx.doi.org/10.1093/geroni/igx004.3239.

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Bos, Angelo Jose Goncalves. "Aging and frailty or frailty and aging?" Geriatrics & Gerontology International 16, no. 7 (July 2016): 880. http://dx.doi.org/10.1111/ggi.12596.

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Gutierrez, Roberto G. "Parametric Frailty and Shared Frailty Survival Models." Stata Journal: Promoting communications on statistics and Stata 2, no. 1 (March 2002): 22–44. http://dx.doi.org/10.1177/1536867x0200200102.

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Guaraldi, Giovanni, and Paolo Raggi. "Atherosclerosis in frailty: Not frailty in atherosclerosis." Atherosclerosis 266 (November 2017): 226–27. http://dx.doi.org/10.1016/j.atherosclerosis.2017.09.014.

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Chao, Yi-Sheng, Chao-Jung Wu, June Y. T. Po, Shih-Yu Huang, Hsing-Chien Wu, Hui-Ting Hsu, Yen-Po Cheng, Yi-Chun Lai, and Wei-Chih Chen. "Frailty does not cause all frail symptoms: United States Health and Retirement Study." PLOS ONE 17, no. 11 (November 2, 2022): e0272289. http://dx.doi.org/10.1371/journal.pone.0272289.

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Background Frailty is associated with major health outcomes. However, the relationships between frailty and frailty symptoms haven’t been well studied. This study aims to show the associations between frailty and frailty symptoms. Methods The Health and Retirement Study (HRS) is an ongoing longitudinal biannual survey in the United States. Three of the most used frailty diagnoses, defined by the Functional Domains Model, the Burden Model, and the Biologic Syndrome Model, were reproduced according to previous studies. The associations between frailty statuses and input symptoms were assessed using odds ratios and correlation coefficients. Results The sample sizes, mean ages, and frailty prevalence matched those reported in previous studies. Frailty statuses were weakly correlated with each other (coefficients = 0.19 to 0.38, p < 0.001 for all). There were 49 input symptoms identified by these three models. Frailty statuses defined by the three models were not significantly correlated with one or two symptoms defined by the same models (p > 0.05 for all). One to six symptoms defined by the other two models were not significantly correlated with each of the three frailty statuses (p > 0.05 for all). Frailty statuses were significantly correlated with their own bias variables (p < 0.05 for all). Conclusion Frailty diagnoses lack significant correlations with some of their own frailty symptoms and some of the frailty symptoms defined by the other two models. This finding raises questions like whether the frailty symptoms lacking significant correlations with frailty statuses could be included to diagnose frailty and whether frailty exists and causes frailty symptoms.

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Chen, Jen-Hau, Hua-San Shih, Jennifer Tu, Jeng-Min Chiou, Shu-Hui Chang, Wei-Li Hsu, Liang-Chuan Lai, Ta-Fu Chen, and Yen-Ching Chen. "A Longitudinal Study on the Association of Interrelated Factors Among Frailty Dimensions, Cognitive Domains, Cognitive Frailty, and All-Cause Mortality." Journal of Alzheimer's Disease 84, no. 4 (December 7, 2021): 1795–809. http://dx.doi.org/10.3233/jad-215111.

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Background: Cognitive frailty integrating impaired cognitive domains and frailty dimensions has not been explored. Objective: This study aimed to explore 1) associations among frailty dimensions and cognitive domains over time and 2) the extended definitions of cognitive frailty for predicting all-cause mortality. Methods: This four-year cohort study recruited 521 older adults at baseline (2011–2013). We utilized 1) generalized linear mixed models exploring associations of frailty dimensions (physical dimension: modified from Fried et al.; psychosocial dimension: integrating self-rated health, mood, and social relationship and support; global frailty: combining physical and psychosocial frailty) with cognition (global and domain-specific) over time and 2) time-dependent Cox proportional hazard models assessing associations between extended definitions of cognitive frailty (cognitive domains-frailty dimensions) and all-cause mortality. Results: At baseline, the prevalence was 3.0% for physical frailty and 37.6% for psychosocial frailty. Greater physical frailty was associated with poor global cognition (adjusted odds ratio = 1.43–3.29, β: –1.07), logical memory (β: –0.14 to –0.10), and executive function (β: –0.51 to –0.12). Greater psychosocial frailty was associated with poor global cognition (β: –0.44) and attention (β: –0.15 to –0.13). Three newly proposed definitions of cognitive frailty, “mild cognitive impairment (MCI)-psychosocial frailty,” “MCI-global frailty,” and “impaired verbal fluency-global frailty,” outperformed traditional cognitive frailty for predicting all-cause mortality (adjusted hazard ratio = 3.49, 6.83, 3.29 versus 4.87; AIC = 224.3, 221.8, 226.1 versus 228.1). Conclusion: Notably, extended definitions of cognitive frailty proposed by this study better predict all-cause mortality in older adults than the traditional definition of cognitive frailty, highlighting the importance of psychosocial frailty to reduce mortality in older adults.

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Hanlon, P., I. Faure, N. Corcoran, E. Butterly, J. Lewsey, D. A. McAllister, and F. S. Mair. "14 A Systematic Review of the Prevalence and Implications of Frailty in Diabetes Mellitus." Age and Ageing 50, Supplement_1 (March 2021): i1—i6. http://dx.doi.org/10.1093/ageing/afab028.14.

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Abstract Background Frailty, an age-related state of vulnerability to adverse health outcomes, is an important factor in the management of diabetes. This systematic review aims to summarise the observational data on prevalence of frailty in diabetes and the association between frailty and generic (e.g. mortality) and diabetes specific (e.g. hypoglycaemia) outcomes. Methods We searched three electronic databases for observational studies assessing frailty in adults (≥18 years) with diabetes (type 1, type 2, or unspecified). Eligible studies quantified the prevalence or incidence of frailty or the association between frailty and clinical outcomes in the context of diabetes. Results 118 studies included, using 18 different frailty measures. Frailty phenotype was the most used (n = 69) followed by frailty index (n = 16) and the FRAIL scale (n = 10). Studies were highly heterogenous in terms of setting (88 community, 18 outpatient, 10 inpatient, 2 residential care), population demographics, and inclusion criteria. The median frailty prevalence in community-based studies using the frailty phenotype was 13% (interquartile range 7-18%). Frailty was identified in “middle-aged” (<65 years) as well as older people with diabetes. Diabetes was consistently associated with incident frailty. Frailty was associated with higher mortality, hospital admission, incident disability. Frailty was associated with hypoglycemic events in 1/1 study. Frailty was also associated cross-sectionally with micro- and macro-vascular complications, lower quality of life, and cognitive impairment. Frailty was not associated with difference in mean HbA1c, however people with frailty were more likely to have high (>9%) or low (<6.5%) HbA1c. Conclusions Frailty in diabetes is common but inconsistently measured. Frailty is associated with a range of adverse outcomes. Research gaps include the relationship between frailty and glycaemia (particularly hypoglycaemia and the relationship between HbA1c and outcomes in the context of frailty), and the impact of frailty in specific groups such as middle-aged people and in low and low-middle income countries.

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Park, Hyungchul, Il-Young Jang, Hea yon Lee, Hee-Won Jung, Eunju Lee, and Dae Hyun Kim. "Screening Value of Social Frailty and Its Association with Physical Frailty and Disability in Community-Dwelling Older Koreans: Aging Study of PyeongChang Rural Area." International Journal of Environmental Research and Public Health 16, no. 16 (August 7, 2019): 2809. http://dx.doi.org/10.3390/ijerph16162809.

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Population aging is a challenge, therefore efficient frailty screening has been increasingly emphasized for mass older populations. This study aimed to evaluate the prevalence of social frailty and its association with physical frailty, geriatric syndromes and activity of daily living (ADL) disability in community-dwelling older adults. A cross-sectional study was conducted with 408 older adults (mean age, 75 years; 58% female) in the Aging Study of PyeongChang Rural Area. A five-item social frailty index was administered (range: 0–5); (1) going out less frequently; (2) rarely visiting the homes of friends; (3) feeling unhelpful to friends and family; (4) being alone; and (5) not talking with someone every day. Social frailty was defined as ≥2 positive responses. Physical frailty was assessed according to the Cardiovascular Health Study frailty phenotype criteria. We used logistic regression to examine whether social frailty can identify older adults with common geriatric syndromes including ADL disability, independently of age, gender, and physical frailty. Social frailty was present in 20.5% (14.5% in male and 25.0% in female) and 11.5% was not overlapped with physical frailty. Social frailty increased risk of ADL disability (odds ratio, 2.53; 95% confidence interval, 1.26–5.09) and depressed mood (odds ratio, 4.01; 95% confidence interval, 1.30–12.39) independently of age, gender, and physical frailty. The predictive power for disability was maximized by using both frailty indices (C statistic 0.73) compared with either frailty index alone (C statistic: 0.71 for social frailty and 0.68 for physical frailty). Social frailty screening is important as it can identify frail older adults who are not captured by demographic characteristics and physical frailty. Moreover, assessment of both social frailty and physical frailty can better detect disability and geriatric syndromes.

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Nguyen, Nguyen, Nguyen, Nguyen, Nguyen, Pham, Tran, et al. "Frailty Prevalence and Association with Health-Related Quality of Life Impairment among Rural Community-Dwelling Older Adults in Vietnam." International Journal of Environmental Research and Public Health 16, no. 20 (October 12, 2019): 3869. http://dx.doi.org/10.3390/ijerph16203869.

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Measuring health-related quality of life (HRQOL) is critical to evaluate the burden of frailty in the older population.This study explored the prevalence of frailty among Vietnamese older people in rural communities, determined the factors associated with frailty, and examined the differences in HRQOL between non-frail, pre-frail, and frail people. A cross-sectional study was conducted on older adults (≥60 years old) residing in Soc Son district, northern Vietnam. Non-frailty, pre-frailty, and frailty conditions were evaluated using Fried’s frailty criteria. The EuroQol-5 Dimensions-5 Levels(EQ-5D-5L) instrument was employed to measure HRQOL. Socioeconomic, behavioral, health status, and healthcare utilization characteristics were collected as covariates. Among 523 older adults, 65.6% were pre-frail, and 21.7% were frail. The mean EQ-5D-5L indexes of the non-frailty, pre-frailty, and frailty groups were 0.70 (SD = 0.18), 0.70 (SD = 0.19), and 0.58 (SD = 0.20), respectively. The differences were found between non-frailty and frailty groups (p < 0.01), as well as the pre-frailty and frailty groups (p<0.01). After adjusting for covariates, the estimated mean difference in the HRQOL between the non-frailty and frailty groups was −0.10 (95%CI= −0.17; −0.02) (R2 = 45.2%), showing a 10% reduction of the maximum EQ-5D-5L index.This study emphasized the high prevalence of frailty among older adults in the rural communities of Vietnam. Frailty was found to be associated with a small reduction of HRQOL in this population.

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Espinoza, Sara, A. R. M. Saifuddin Ekram, Robyn Woods, Michael Ernst, Galina Polekhina, John McNeil, Anne Murray, and Joanne Ryan. "The Effect of Low-Dose Aspirin on Frailty in Older Adults in the Aspirin in Reducing Events in the Elderly Study." Innovation in Aging 5, Supplement_1 (December 1, 2021): 819–20. http://dx.doi.org/10.1093/geroni/igab046.3009.

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Abstract There are no widely accepted pharmacologic treatments for frailty prevention. Since frailty is associated with inflammation, aspirin has the potential to reduce frailty. We investigated whether low-dose aspirin reduces incident frailty in participants of the ASPirin in Reducing Events in the Elderly (ASPREE) trial. In the U.S and Australia, 19,114 healthy community-dwelling individuals aged ≥70 years (U.S. minorities ≥65 years) were enrolled in ASPREE, a double-blind, placebo-controlled trial of 100mg daily low-dose aspirin vs. placebo. Frailty was defined according to a modified Fried frailty definition, and a frailty index which used a deficit accumulation model. Competing risk Cox proportional hazards models were used to compare time to incident frailty for aspirin vs. placebo. At baseline, 2.2% and 8.1% met criteria for frailty by Fried and frailty index criteria, respectively. Over a median of 4.7 years of follow-up, 2252 participants developed incident frailty according to Fried classification, and 4376 according to the frailty deficit accumulation index. There was no difference in the risk of incident frailty between individuals randomized to aspirin versus placebo according to either criteria (Fried frailty HR: 1.03, 95% CI 0.97-1.09, p=0.41; frailty index HR: 1.03, 95% CI 0.97-1.10, p=0.29). Change in frailty over time was not different between the aspirin and placebo treatment arms. The results were consistent across a series of sub-groups, including baseline frailty status. Based on these results, aspirin use in healthy older adults does not reduce incident frailty.

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Gobbens, Robbert J., Sofie Vermeiren, An Van Hoof, and Tjeerd van der Ploeg. "Nurses’ Opinions on Frailty." Healthcare 10, no. 9 (August 26, 2022): 1632. http://dx.doi.org/10.3390/healthcare10091632.

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Nurses come into frequent contact with frail older people in all healthcare settings. However, few studies have specifically asked nurses about their views on frailty. The main aim of this study was to explore the opinions of nurses working with older people on the concept of frailty, regardless of the care setting. In addition, the associations between the background characteristics of nurses and their opinions about frailty were examined. In 2021, members of professional association of nurses and nursing assistants in the Netherlands (V&VN) received a digital questionnaire asking their opinions on frailty, and 251 individuals completed the questionnaire (response rate of 32.1%). The questionnaire contained seven topics: keywords of frailty, frailty domains, causes of frailty, consequences of frailty, reversing frailty, the prevention of frailty, and addressing frailty. Regarding frailty, nurses especially thought of physical deterioration and dementia. However, other domains of human functioning, such as the social and psychological domains, were often mentioned, pointing to a holistic approach to frailty. It also appears that nurses can identify many causes and consequences of frailty. They see opportunities to reverse frailty and an important role for themselves in this process.

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Angioni, Davide, T. Macaron, C. Takeda, S. Sourdet, M. Cesari, K. Virecoulon Giudici, J. Raffin, et al. "Can We Distinguish Age-Related Frailty from Frailty Related to Diseases? Data from the MAPT Study." Journal of nutrition, health & aging 24, no. 10 (October 27, 2020): 1144–51. http://dx.doi.org/10.1007/s12603-020-1518-x.

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Abstract Background No study has tried to distinguish subjects that become frail due to diseases (frailty related to diseases) or in the absence of specific medical events; in this latter case, it is possible that aging process would act as the main frailty driver (age-related frailty). Objectives To classify subjects according to the origin of physical frailty: age-related frailty, frailty related to diseases, frailty of uncertain origin, and to compare their clinical characteristics. Materials and methods We performed a secondary analysis of the Multidomain Alzheimer Preventive Trial (MAPT), including 195 subjects ≥70 years non-frail at baseline who became frail during a 5-year follow-up (mean age 77.8 years ± 4.7; 70% female). Physical frailty was defined as presenting ≥3 of the 5 Fried criteria: weight loss, exhaustion, weakness, slowness, low physical activity. Clinical files were independently reviewed by two different clinicians using a standardized assessment method in order to classify subjects as: “age-related frailty”, “frailty related to diseases” or “frailty of uncertain origin”. Inconsistencies among the two raters and cases of uncertain frailty were further assessed by two other experienced clinicians. Results From the 195 included subjects, 82 (42%) were classified as age-related frailty, 53 (27%) as frailty related to diseases, and 60 (31%) as frailty of uncertain origin. Patients who became frail due to diseases did not differ from the others groups in terms of functional, cognitive, psychological status and age at baseline, however they presented a higher burden of comorbidity as measured by the Cumulative Illness Rating Scale (CIRS) (8.20 ± 2.69; vs 6.22 ± 2.02 frailty of uncertain origin; vs. 3.25 ± 1.65 age-related frailty). Time to incident frailty (23.4 months ± 12.1 vs. 39.2 ± 19.3 months) and time spent in a pre-frailty condition (17.1 ± 11.4 vs 26.6 ± 16.6 months) were shorter in the group of frailty related to diseases compared to age-related frailty. Orthopedic diseases (n=14, 26%) were the most common pathologies leading to frailty related to diseases, followed by cardiovascular diseases (n=9, 17%) and neurological diseases (n = 8, 15%). Conclusion People classified as age-related frailty and frailty related to diseases presented different frailty-associated indicators. Future research should target the underlying biological cascades leading to these two frailty classifications, since they could ask for distinct strategies of prevention and management.

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Fons, Anne, Kees Kalisvaart, and Jeroen Maljaars. "Frailty and Inflammatory Bowel Disease: A Scoping Review of Current Evidence." Journal of Clinical Medicine 12, no. 2 (January 9, 2023): 533. http://dx.doi.org/10.3390/jcm12020533.

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Frailty is increasingly recognized as an important concept in patients with Inflammatory Bowel Disease (IBD). The aim of this scoping review is to summarize the current literature on frailty in IBD. We will discuss the definition of frailty, frailty assessment methods, the prevalence of frailty, risk factors for frailty and the prognostic value of frailty in IBD. A scoping literature search was performed using the PubMed database. Frailty prevalence varied from 6% to 53.9%, depending on the population and frailty assessment method. Frailty was associated with a range of adverse outcomes, including an increased risk for all-cause hospitalization and readmission, mortality in non-surgical setting, IBD-related hospitalization and readmission. Therefore, frailty assessment should become integrated as part of routine clinical care for older patients with IBD.

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Takeda, Tomonori, Atsuhiro Tsubaki, Yoshifumi Ikeda, Ritsushi Kato, Kazuki Hotta, Tatsuro Inoue, Sho Kojima, et al. "The impacts of preoperative frailty on readmission after cardiac implantable electrical device implantation." PLOS ONE 17, no. 11 (November 3, 2022): e0277115. http://dx.doi.org/10.1371/journal.pone.0277115.

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Cardiac implantable electrical devices (CIED) such as pacemakers, implantable cardioverter defibrillators, and cardiac resynchronization therapies are generally recommended for older patients and those with severe heart failure (HF). However, there is currently a lack of evidence on the relationship between frailty and readmission rates among patients with CIED. This study investigated whether preoperative frailty influenced readmission rates among patients with CIED over a one-year period following implantation. The study retrospectively analyzed 101 patients who underwent CIED implantations. To compare frailty-based differences in their characteristics and readmission rates, these participants were categorized into frailty and non-frailty groups via the modified frailty index (mFI). The frailty group had a significantly higher readmission rate than the non-frailty group (non-frailty group vs. frailty group = 1 vs. 8 patients: P < 0.05). Further, a multivariate analysis showed that frailty was a significant readmission factor. Based on individual analyses with/without histories of HF, the readmission rate also tended to be higher among individuals considered frail via the mFI (readmission rate in HF patients: non-frailty group vs. frailty group = 1 vs. 5 patients: P = 0.65; non-HF patients: non-frailty group vs. frailty group = 0 vs. 3 patients: P = 0.01). Participants with preoperative frailty showed higher readmission rates within a one-year period following implantation compared to those without preoperative frailty. This tendency was consistent regardless of HF history. The mFI may thus help predict readmission among patients with CIED.

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Arizaga-Iribarren, Nagore, Amaia Irazusta, Itxaso Mugica-Errazquin, Janire Virgala-García, Arantxa Amonarraiz, and Maider Kortajarena. "Sex Differences in Frailty Factors and Their Capacity to Identify Frailty in Older Adults Living in Long-Term Nursing Homes." International Journal of Environmental Research and Public Health 20, no. 1 (December 21, 2022): 54. http://dx.doi.org/10.3390/ijerph20010054.

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Frailty is a phenomenon that precedes adverse health events in older people. However, there is currently no consensus for how to best measure frailty. Several studies report that women have a higher prevalence of frailty than men, but there is a gap in studies of the high rates of frailty in older people living in long-term nursing homes (LTNHs) stratified by sex. Therefore, we analyzed health parameters related to frailty and measured their capacity to identify frailty stratified by sex in older people living in LTNHs. According to the Fried Frailty Phenotype (FFP), anxiety increased the risk of frailty in women, while for men functionality protected against the risk of frailty. Regarding the Tilburg Frailty Indicator (TFI), functionality had a protective effect in men, while for women worse dynamic balance indicated a higher risk of frailty. The analyzed parameters had a similar capacity for detecting frailty measured by the TFI in both sexes, while the parameters differed in frailty measured by the FFP. Our study suggests that assessment of frailty in older adults should incorporate a broad definition of frailty that includes not only physical parameters but also psycho-affective aspects as measured by instruments such as the TFI.

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Limpawattana, Panita, Chudapha Khammak, Manchumad Manjavong, and Apichart So-ngern. "Frailty as a Predictor of Hospitalization and Low Quality of Life in Geriatric Patients at an Internal Medicine Outpatient Clinic: A Cross-Sectional Study." Geriatrics 7, no. 5 (August 31, 2022): 89. http://dx.doi.org/10.3390/geriatrics7050089.

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Frailty is an aging-associated state that increases patients’ vulnerability to disease, and can lead to various adverse outcomes. It is classified as either physical frailty alone or physical frailty in combination with cognitive impairment (cognitive frailty). There are currently limited data available regarding the prevalence and adverse outcomes of frailty in Thailand. This was a cross-sectional study aimed at determining the prevalence of physical and cognitive frailty and their effects on hospitalization and quality of life. Participants were older patients who attended an internal medicine outpatient clinic. Frailty was diagnosed using the Thai Frailty Index. The Thai version of the MoCA was used to evaluate cognitive status. Univariate and multivariate analyses were performed to compare adverse outcomes in terms of poor quality of life and history of admission to hospital between patients with frailty and non-frail patients, and among patients with physical frailty, cognitive frailty, cognitive impairment, and robust (non-frail and non-cognitively impaired) patients. We enrolled 198 participants. The prevalence of physical and cognitive frailty was 28.78% and 20.70%, respectively. When compared with non-frail patients, frailty was associated with hospitalization (adjusted OR 3.01, p = 0.002) but was not significantly related to quality of life (adjusted OR = 1.98, p = 0.09). However, physical and cognitive frailty were associated with fair quality of life when compared with normal patients (adjusted OR = 4.34, p = 0.04 and adjusted OR = 4.28, p = 0.03, respectively). The prevalence of frailty—particularly cognitive frailty—was high. Frailty was associated with adverse outcomes in terms of hospitalization and quality of life.

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Nishimoto, Misa, Tomoki Tanaka, Yutaka Watanabe, Hirohiko Hirano, Takeshi Kikutani, Tetsuro Sato, Kazuko Nakajo, and Katsuya Iijima. "ORAL FRAILTY IS ASSOCIATED WITH MULTIFACETED FRAILTY IN ELDERLY OUTPATIENTS AT COMMUNITY DENTAL CLINICS." Innovation in Aging 3, Supplement_1 (November 2019): S147. http://dx.doi.org/10.1093/geroni/igz038.530.

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Abstract Aim For achieving healthy aging, frailty prevention is essential. Because it is reported that accumulated declines in multiple oral functions (i.e. oral frailty) could lead to frailty progression, detailed countermeasures for oral frailty are currently required. However, dentists of community dental clinics don’t even know a prevalence of oral frailty among outpatients. Thus, we aimed to identify the prevalence of oral frailty and to examine the association with frailty in outpatients at community dental clinics. Methods The subjects were elderly outpatients at dental clinics in Kanagawa, Japan. Frailty was assessed using the Kihon checklists (KCL); those with ≥8 KCL score were classified as frailty. Furthermore, multiple functions (physical, nutrition, and oral) were assessed using subscale of the KCL. Oral frailty was defined as ≥3 deteriorations out of 5 oral status (remaining teeth, chewing ability, articulatory oral motor skill, subjective difficulties in eating and swallowing). Results Of 1,699 outpatients (mean age, 75 ± 6.3 years old; 40% men), 12% were frailty and 21% were oral frailty. When adjusted by confounding factors such as age and sex, those with oral frailty were associated with higher prevalence of frailty (OR, 3.25; 95%CI, 2.34-4.53), decreased physical and oral functions (OR, 1.53; 95%CI, 1.07-2.16: OR, 8.14; 95%CI, 6.05-10.95, respectively). Conclusions Oral frailty was associated with multi-faceted frailty in outpatients at community dental clinics. In addition to the importance of maintenance of whole oral functions including treating teeth, our findings suggest that it is also indispensable to consider the multi-faceted frailty for elderly patients.

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Mazonson, Peter, Theoren Loo, Jeff Berko, Sarah-Marie Chan, Ryan Westergaard, and James Sosman. "352. Characteristics Associated with Pre-Frailty in Older People Living with HIV." Open Forum Infectious Diseases 6, Supplement_2 (October 2019): S186. http://dx.doi.org/10.1093/ofid/ofz360.425.

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Abstract Background Frailty is a concern among older people living with HIV (PLHIV). There is a paucity of research characterizing PLHIV who are at risk of becoming frail (pre-frailty). To investigate how HIV impacts older PLHIV in the United States, a new study called Aging with Dignity, Health, Optimism and Community (ADHOC) was launched at ten sites to collect self-reported data. This analysis uses data from ADHOC to identify factors associated with pre-frailty. Methods Pre-frailty was assessed using the Frailty Index for Elders (FIFE), where a score of zero indicated no frailty, 1–3 indicated pre-frailty, and 4–10 indicated frailty. A cross-sectional analysis was performed on 262 PLHIV (age 50+) to determine the association between pre-frailty and self-reported sociodemographic, health, and clinical indicators using bivariate analyses. Factors associated with pre-frailty were then included in a logistic regression analysis using backward selection. Results The average age of ADHOC participants was 59 years. Eighty-two percent were male, 66% were gay or lesbian, and 56% were white. Forty-seven percent were classified with pre-frailty, 26% with frailty, and 27% with no frailty. In bivariate analyses, pre-frailty was associated with depression, low cognitive function, depression, multiple comorbidities, low income, low social support and unemployment (Table 1). In the multiple logistic regression analysis, pre-frailty was associated with having low cognitive function (Odds Ratio [OR] 8.56, 95% Confidence Interval [CI]: 3.24–22.63), 4 or more comorbid conditions (OR 4.00, 95% CI: 2.23–7.06), and an income less than $50,000 (OR 2.70, 95% CI: 1.56–4.68) (Table 2). Conclusion This study shows that commonly collected clinical and sociodemographic metrics can help identify PLWH who are more likely to have pre-frailty. Early recognition of factors associated with pre-frailty among PLHIV may help to prevent progression to frailty. Understanding markers of increased risk for pre-frailty may help clinicians and health systems better target multi-modal interventions to prevent negative health outcomes associated with frailty. Disclosures All authors: No reported disclosures.

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Sacha, Magdalena, Jerzy Sacha, and Katarzyna Wieczorowska-Tobis. "Determinants of Multidimensional and Physical Frailty and Their Individual Components: Interactions between Frailty Deficits." International Journal of Environmental Research and Public Health 17, no. 22 (November 21, 2020): 8656. http://dx.doi.org/10.3390/ijerph17228656.

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Purpose: To identify the interrelations among determinants of multidimensional frailty, physical frailty, and their individual components. Methods: A group of 1024 community-dwelling people older than 65 years completed questionnaires regarding: multidimensional frailty (Tilburg Frailty Indicator, TFI) and physical frailty (FRAIL scale), and common frailty risk factors. Results: Multidimensional frailty was recognized in 559 subjects (54.6%) and determined by 13 factors (R2 = 0.21 in logistic regression). After incorporating TFI components to the models, the majority of previous risk factors became non-essential, and the frailty deficits mainly determined each other with R2 ranging between 0.07–0.67. Physical frailty and non-robust status (i.e., either physical frailty or pre-frailty) were recognized in 64 (6.3%) and 542 (52.9%) participants, and were determined by 5 factors (R2 = 0.33) and 11 factors (R2 = 0.34), respectively. Associations between the frailty deficits were detected within and between different dimensions (i.e., physical, psychological and social); the physical domain was mainly related to the psychological one which in turn was additionally associated with the social one. Conclusion: Frailty is the accumulation of deficits and is determined by factors other than the determinants of the individual deficits. The associations between deficits coming from various dimensions of human functioning presumably amplify their effects and accelerate frailty development.

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