• Users Online: 341
  • Print this page
  • Email this page


 
 
Table of Contents
ORIGINAL ARTICLE - GERIATRIC ONCOLOGY SECTION
Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 75-82

Timed Up and Go as a predictor of mortality in older Indian patients with cancer: An observational study


1 Department of Medical Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
2 Department of Clinical Pharmacology, Advanced Centre for Treatment Research and Education in Cancer, Mumbai, Maharashtra, India
3 Department of Physiotherapy, Tata Memorial Hospital, Mumbai, Maharashtra, India

Date of Submission02-Sep-2022
Date of Decision03-Oct-2022
Date of Acceptance03-Oct-2022
Date of Web Publication31-Mar-2022

Correspondence Address:
Kumar Prabhash
Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai - 400 012, Maharashtra
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/crst.crst_79_22

Rights and Permissions
  Abstract 


Background: Therapeutic decision-making in older patients with cancer is challenging, and there is a need for a clinical parameter that can guide these decisions. The Timed Up and Go (TUG) test is an easy-to-administer tool that measures physical performance and may help to identify vulnerable patients.
Objectives: We aimed to study the association between the TUG and overall survival in older Indian patients with cancer. We also aimed to evaluate the factors that were associated with a poor TUG score, to define the optimal cut-off score for older patients with cancer, along with the sensitivity and specificity.
Materials and Methods: This prospective observational study was conducted in the geriatric oncology clinic at the Tata Memorial Hospital, a tertiary care center in Mumbai, India, between June 2018 and January 2022. We included adults aged 60 years and above, who had a diagnosis of malignancy, and had undergone a multidimensional geriatric assessment. Patients without a TUG score and for whom survival information was not available were excluded. Using the Short Physical Performance Battery (SPPB) as the gold standard, Receiver Operating Characteristic (ROC) curves with Area Under the Curve (AUC) were used, and the cut-off score with optimum sensitivity and specificity was derived. Mean values between two and more groups were compared using t test and analysis of variance, respectively. Categorical variables were compared using Fisher's exact and Pearson's Chi-squared test. The Kaplan–Meier survival estimate, and the unadjusted and adjusted Cox proportional-hazards model were used for survival analysis.
Results: We enrolled 851 patients in the study. The median age was 69 (IQR, 65 to 73) years; 544 patients (76%) were men. We found that the TUG in women (median TUG, 11 seconds; IQR, 9.5 - 13.7) was longer than in men (median TUG, 9.6 seconds; IQR, 8.2 -11.6); P < 0.01. The TUG score increased significantly with increasing age in both sexes. Presence of comorbidities impaired cognition, poor nutritional status, depression, and anxiety were significantly associated with higher TUG scores. TUG was a significant predictor of mortality on both the univariate (HR, 1.056; 95% CI, 1.037–1.075) and multivariate models (HR, 1.058; 95% CI, 1.039–1.078). The median survival of patients with TUG <12 seconds was 13.9 months (95% CI, 11.2 to 16.5), compared to 8.5 months (95% CI, 6.6 to 10.3) in those with a TUG ≥ 12 seconds (P = 0.002). The TUG cut-off score of 10 seconds had an AUC-ROC, sensitivity of 62.32% and specificity of 80.58%.
Conclusion: TUG can be a reliable tool in a busy outpatient setting to identify vulnerable patients who require a detailed geriatric assessment. A TUG score of ≥ 10 seconds is a good predictor of impaired mobility. Further 0.78 interventional studies are required to identify the benefits of physical therapy in older patients with cancer.

Keywords: Geriatric oncology, mobility, Timed up and Go, TUG


How to cite this article:
Rao AR, Kumar S, Dhekale R, Krishnamurthy J, Mahajan S, Daptardar A, Ramaswamy A, Noronha V, Gota V, Banavali S, Prabhash K. Timed Up and Go as a predictor of mortality in older Indian patients with cancer: An observational study. Cancer Res Stat Treat 2022;5:75-82

How to cite this URL:
Rao AR, Kumar S, Dhekale R, Krishnamurthy J, Mahajan S, Daptardar A, Ramaswamy A, Noronha V, Gota V, Banavali S, Prabhash K. Timed Up and Go as a predictor of mortality in older Indian patients with cancer: An observational study. Cancer Res Stat Treat [serial online] 2022 [cited 2022 May 21];5:75-82. Available from: https://www.crstonline.com/text.asp?2022/5/1/75/341243






  Introduction Top


With the increasing life expectancy, aging of the population, and improvement in cancer screening, the incidence of cancer has risen.[1–3] Persons aged over 65 years account for 60% of newly diagnosed malignancies and 70% of cancer-related deaths.[4] The treatment outcomes of vulnerable patients with cancer may be different from those of their healthy counterparts. The challenging decision-making process can be aided by the results of a geriatric assessment (GA), which helps identify susceptible persons.

Differentiating a robust from a frail person based on the assessment of multiple aspects of the aging process is imperative. The two frailty models include the phenotypic[5] and the deficit accumulation model.[6] The former proposes a syndrome of weight loss, a decline in activities or exhaustion, and slowing of the gait speed. In contrast, the latter suggests that as more health deficits accumulate, the risk of adverse outcomes increases. Though frailty assessment is a powerful tool, the constraints in time and resources required for complete assessment prevents the practicing oncologist from utilizing it in their routine practice.[7]

Timed Up and Go (TUG) is a simple tool that is used for functional assessment in order to identify vulnerable older adults.[8] TUG and gait speed are helpful to predict falls.[5],[9] Further, they are independently associated with greater mortality risk, both in the presence and absence of cardiovascular risk factors.[10–12] Studies have measured the association between the TUG score and clinical outcomes such as chemotherapy-related toxicity,[13] falls,[14] functional decline[15] and post-surgical morbidity.[16] However, only one study has reported a significant association between the TUG score and survival.[17]

It is unclear whether this mortality risk holds true in all older patients with cancer, especially those living in low- and middle-income countries such as India. Some of the tools developed in Western populations and used for assessing geriatric domains may not be directly applicable to patients in low- and middle-income countries.[18] We aimed to bridge this gap by analyzing the association of the TUG score with mortality and to determine the optimal TUG score in our setup to predict impaired mobility.


  Methods Top


General study details

This prospective study was conducted between June 2018 and January 2022 in the geriatric oncology clinic of the Tata Memorial Hospital, a tertiary care cancer center in Mumbai, India. The clinic is a multidisciplinary clinic, where the patients undergo a GA, following which treatment recommendations and follow-up are planned. The assessment team includes a medical oncologist, geriatrician, clinical pharmacologist, psychiatrist, dietician, physiotherapist, occupational therapist, and social worker.[19] The Institutional Ethics Committee of Tata Memorial Center approved the study (project 900596; approved on Mar 20, 2020) [Supplementary Appendix 1]. The need to obtain written informed consent was waived for the retrospective portion of the study; patients enrolled in the prospective portion of the study provided written informed consent prior to enrollment. This study was conducted according to ethical guidelines established by the Declaration of Helsinki and Good Clinical Practice Guidelines. The study was registered with the Clinical Trials Registry-India (CTRI/2020/04/024675). No funding was used for the study.

Participants

Older adults aged 60 years and over with a diagnosis of cancer, who were evaluated in the geriatric oncology clinic and had the TUG score recorded were included. Patients who were lost to follow-up, that is, for whom survival outcomes were not available were excluded.

Variables

Our primary objective was to study the association of the TUG score with survival. The secondary objective was to evaluate the clinical profile of the patients, the risk factors that were associated with poor TUG scores, and to define the optimal cut-off score for TUG in older Indian patients with cancer.

Study methodology

Patients with cancer aged 60 years and above underwent a multidisciplinary geriatric assessment which included recording their demographic details, anthropometric measurements, height, weight, body mass index (BMI), and weight loss. Assessed geriatric domains included comorbidities assessed by the Cumulative Illness Rating Scale-Geriatric (CIRS-G), cognition assessed by the mini-mental status examination (MMSE) for literate and Hindi-mental status examination (HMSE) for illiterate patients, nutrition assessed by the Mini-Nutritional Assessment (MNA), and psychological assessment using the Geriatric Depression Scale (GDS) and generalized anxiety disorder 7 (GAD 7). The presence of potentially inappropriate medications was evaluated using Beer's criteria; polypharmacy was defined as the presence of five or more medications. Functional status was evaluated using Katz Activities of Daily Living (ADL), Lawton Instrumental Activities of Daily Living (IADL) and TUG. Social support was evaluated by Older Americans Resources and Services Medical Social Support (OARS-MSS). For falls, we asked the patients if they had experienced a fall(s) in the preceding year. The Cancer and Aging Research Group (CARG) tool was used to assess the risk of chemotherapy-related toxicity.

Impaired cognition was defined as a score of less than 24. Impaired CIRS-G was defined as a score greater than 6. Nutritional status was classified based on the MNA score as malnourished (<17), at risk for malnourishment (17–23) and normal (24–30). A GDS score greater than 5 and GAD score greater than 9 were taken as screen positive for depression and anxiety, respectively. The CARG score was calculated using an online calculator which required entering the patient's age, sex, height, weight, cancer type, dose and number of chemotherapy agents, hemoglobin, serum creatinine, hearing impairment, falls in past 6 months, ability to take own medicines, limitation in walking one block and interference with social activities. The score ranges between 0 to 19. CARG score risk groups were as per the original study by Hurria et al.[20]: low risk (score 0–5), intermediate risk (score 6–9), and high risk (score ≥10).

Assessment of TUG

The participant was asked to stand up from a chair, walk a distance of three meters, touch an object like the door handle, walk back to the chair, and sit down. The examiner completed a demonstration of the TUG measure before having the patient complete the measure. The timing was measured from when the examiner said “Go” and stopped when the participant sat back down on the chair. A single measure was taken. A score of 12 seconds or more was considered as impaired TUG.[21] Short Physical Performance Battery (SPPB), a tool used to predict disability and for monitoring function in older adults, was evaluated by the physiotherapy team. The components included standing balance assessed using tandem, semi-tandem and side-by-side standing, gait speed and chair-rise test. The score ranged from 0 to 12 and a score of less than 10 was considered abnormal.

Statistics

No sample size calculation was performed for this study. All analyses were performed using the Statistical Package for the Social Sciences (IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp) and STATA version 14 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). The mean values for continuous variables were compared between groups using an independent t test for two groups and analysis of variance (ANOVA) for more than two groups, while categorical data were compared using Chi-squared tests.

Kaplan–Meier survival curves were compared using the log-rank test. Cox proportional-hazards model was used to assess the effect of TUG score to obtain corresponding hazard ratio (HR) and 95% confidence intervals CI. For subjects who were lost to follow up, survival time until the last date of contact was entered. Patients were censored on the date of the last follow up. Multivariable Cox proportional-hazards models incorporated adjustment for all baseline covariates with a statistically significant (P ≤ .05) univariate association with the outcome. The reverse Kaplan–Meier estimate was used to calculate the median follow-up period. To define the TUG cut-off, using SPPB as the gold standard, Receiver Operating Characteristic (ROC) curves with Area Under the Curve (AUC) were used. A higher AUC value indicated that a measure could predict impaired mobility ranging from 0.5, where the test was not better than chance, up to 1.0, meaning that the test had a 100% ability to predict impairment. The score with the optimum sensitivity and specificity to predict impaired mobility was determined.


  Results Top


Of the 910 patients evaluated in the clinic [Figure 1], 851 patients were included. The median age was 69 years (IQR, 65-73), and 544 were men (76%). Complete demographic information is provided in [Table 1]. The median TUG in women was 11 seconds (IQR, 9.5 - 13.7) compared to that in men (9.6 sec, IQR, 8.2 - 11.6) (P < 0.01). The TUG scores increased substantially with increasing age, which was significant in both sexes. Patients with comorbidities such as hypertension, chronic obstructive pulmonary disease and cardiovascular disease were significantly slower. Patients with a history of falls (n = 86) had a median TUG score of 11.5 seconds (IQR, 9.2 - 14.8) compared to those with no prior falls (median TUG of 9.8 sec; IQR, 8.4 - 11.9) (P < 0.01).
Table 1: Baseline characteristics of the study population (n=851)

Click here to view
Figure 1: Scheme of the inclusion of participants in the study

Click here to view


Patients with impaired cognition, comorbidities, poor nutritional status, depression, and anxiety had significantly higher TUG scores. Additionally, patients with a high risk of chemotherapy-related toxicity, as evaluated by the CARG chemotherapy toxicity risk prediction tool were slower (median TUG, 11.5 sec, IQR, 9.4 - 14.5) compared to those with intermediate-risk (median TUG, 9.5 sec; IQR, 8.3 - 11.1) and low risk (median TUG, 8.8 sec, IQR, 7.4 -10.0) (P < 0.01).

Association with mortality

Using the prespecified cut-off of 12 seconds [Figure 2], we found that the median survival among participants with a normal TUG score (13.9 months [95% CI, 11.2–16.5]) was significantly longer compared to those with a TUG score of ≥12 seconds (8.5 months [95% CI, 6.6–10.3]) (P = 0.002).
Figure 2: Kaplan–Meier curves displaying the estimated survival probability of patients with TUG score of <12 seconds (red); median survival was 13.9 months and ≥12 seconds (blue) with median survival of 8.5 months, P = 0.002

Click here to view


The regression analysis results to predict mortality can be found in [Table 2]. The median follow-up was 9.9 months (range, 5.1 - 18.2). After fitting the data to the model, it was determined that the TUG was a key predictor variable influencing mortality probability (HR = 1.056, 95% CI, 1.037–1.075) in the base model (univariate analysis). A multivariate Cox proportional-hazards model was also performed to evaluate the significant covariates derived from the individual univariate analysis. From this multivariate analysis, we determined that sex, age, and weight loss did not significantly affect survival. In the presence of confounders, the adjusted TUG score (HR, 1.058, 95% CI, 1.039–1.078) did not change the HR compared to the unadjusted score (HR, 1.056).{Table 12}

ROC curve, sensitivity and specificity

The ROC is shown in [Figure 3]. TUG scores had an AUC-ROC of 0.78 (95% CI, 0.72–0.83). The sensitivity and specificity of each measure at different cut-offs were reviewed. The TUG cut-off score of 10 seconds had a sensitivity of 62.32% and specificity of 80.58%, whereas when using the American Society of Clinical Oncology (ASCO) recommended cut-off for TUG of 12 seconds,[21] the sensitivity and specificity were 25.1% and 100%, respectively.
Figure 3: Receiver operating characteristic (ROC) curve of the cut-off point of TUG score

Click here to view



  Discussion Top


The mobility measurements predict the risk of falls, functional dependence, and survival in older adults. In patients with cancer, the identification of prognostic factors is helpful to inform treatment choices and provide valuable information to patients. This identification is more relevant in managing older patients with cancer due to concerns about the generalizability of treatment outcomes that have been tested and reported in younger patients Due to the unique medical history and physical impairments in older patients with cancer, establishing cut-off scores for mobility measures needed in this vulnerable population. To the best of our knowledge, ours is the first study to evaluate the use of TUG among older Indian patients with cancer, and this study adds to the literature describing the utility of TUG scores to predict mortality.

In unadjusted and adjusted multivariate regression modelling, TUG scores, examined as a continuous variable, were identified as a significant predictor of mortality. Patients with a TUG score of <12 seconds had a significantly longer median overall survival than those with higher scores. Physical performance and function, measured using various tools, is a known predictor of survival.[22],[23] Out of the seven studies which measured TUG score as a part of GA in patients with cancer, five found an association between prolonged TUG score and survival on the univariate analysis. [17,24–27] One study found an association only after correcting for potential confounders in the multivariate analysis,[25] and another reported a significant association in both the univariate and multivariate analyses.[17] Some of these studies included patients with a single cancer type, such as lung[24] or hematological malignancies, while our study included patients with a wide range of solid tumors.[25],[28]

The cut-off scores used for TUG also varied widely, ranging from 10 seconds[13] to 20 seconds,[15],[16],[28] whereas the cut-ff was not clearly stated in two studies.[24],[27] These findings underline the fact that there is no consensus regarding the optimal cut-off for TUG. Reasons for the lack of consensus regarding the optimal TUG cut-off could include the fact that TUG was used to identify vulnerable older persons at increased risk of falls in different settings, including the community and during outpatient visits.[9],[29] In our study, the ROC to determine the TUG cut-off score for our patients had an AUC of 0.78 indicating the measure had a fair ability to predict impaired function. The cut-off score of 10 seconds had a sensitivity of 62.32% and specificity of 80.58%; the sensitivity decreased to 25.1% at the cut-off score of 12 seconds, making 10 seconds a more appropriate cut-off score for older Indian patients with cancer. Clinicians should consider using this cut-off as a benchmark to assess the functional status in older patients with cancer. Though few studies have tried to define the cut-off for mobility parameters in older Indian adults without cancer,[30],[31] we were unable to find any previous studies from India in older patients with cancer.

Advancing age is associated with diminution of physical performance.[32],[33] In our study as well, we found that the TUG scores increased with advancing age. Previous studies conducted in the community have reported that the mean TUG score increased with increasing age,[34] with one study reporting that the TUG score increased by 0.14 seconds for every 1 year after the age of 65 years.[35] We also found a higher prevalence of impaired scores among older women than men, replicating the findings from previous studies.[36] However, past studies among older patients with cancer did not report such an association.[13],[15],[16]

Physical performance measures, including TUG, can assess a preclinical level of disability characterized by early functional limitation.[37],[38] These measures can be considered as indicators of the extent of cumulative age-related body changes and disease burden. Previous studies have reported that performance in the TUG test was capable of identifying participants at risk of developing comorbidities,[39] and stratifying patients with chronic obstructive pulmonary disease[40] and cardiovascular disease.[5],[41] Our study also reported the association between higher TUG scores and the presence of comorbidities such as hypertension, chronic obstructive pulmonary disease, and cardiovascular disease. We found higher TUG scores among patients with cognitive decline and emotional dysfunction. Various cross-sectional and longitudinal studies have examined the relationship between objective measures of cognition and physical function in later life,[42] and a pre-dementia state called motoric cognitive risk syndrome[43] ties the previously conceived separate domains (cognition and physical performance) together.

Our study adds to the literature, as it describes a tool to assess mortality risk in older patients with cancer; however, if scores of the included measure in this study are impaired (prolonged TUG score), follow-up interventions are suggested. Multimodal exercise programs have been shown to reduce the risk of falls among cancer survivors.[44],[45] Data also indicate that physical exercise positively affects mortality and recurrence rates in patients with cancer.[46],[47] However, the studies included in these systematic reviews also included younger patients. Nevertheless, additional research should be conducted to determine specific types and parameters of exercise to improve mobility and decrease the risk of mortality among older cancer patients.

This is the first study to report an association between TUG scores and mortality among older Indian patients with cancer and determine the optimum TUG cut-off score to predict impaired mobility. As our institute is a tertiary referral center, the inclusion of patients from various states across India, the prospective nature, large sample size, and inclusion of patients with multiple cancer sites increase the general applicability of our results. The limitations of our study are that treatments were not included among confounders, and we assumed the best available therapies were used in all patients. Data about the causes of death were not available; hence we could not investigate if the TUG measure predicted cancer-specific death and death from other causes. Though patients with various tumor sites and stages were included, the patients with each different tumor were too small for subgroup analyses. Inclusion of older patients with cancer referred for a GA may have introduced selection bias.


  Conclusion Top


When assessing older patients with cancer, measurement of the TUG may be a simple tool to assess mortality risk. Oncologists may consider using it to stratify, prognosticate, and aid in managing this vulnerable population. Patients with a poor score on the TUG test would require further detailed evaluation by a multidisciplinary team to provide better and informed care. Further interventional studies are required to identify the benefits on physical therapy in older patients with cancer.

Data sharing statement:

The study protocol has been included as a supplementary appendix along with this article. The de-identified individual patient data are available on reasonable request from Dr Vanita Noronha, by email <[email protected]>. The data will be available starting from the date of publication onwards.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.


  Supplementary Appendix 1-Study Protocol Top


Clinical Study Protocol

RETROSPECTIVE AND PROSPECTIVE ANALYSIS OF THE RESULTS OF A COMPREHENSIVE GERIATRIC ASSESSMENT OF OLDER PATIENTS WITH CANCER WHO HAVE BEEN EVALUATED IN THE GERIATRIC ONCOLOGY CLINIC AT TATA MEMORIAL HOSPITAL

Introduction

The world's older population continues to grow at an unprecedented rate. Today, 8.5% of people worldwide (617 million) are aged 65 and over. “An Aging World: 2015” reported that this proportion will increase to almost 17% of the world's population by 2050, or 1.6 billion[1]. According to a 2016 report by the Ministry for Statistics and Programme Implementation, India has 103.9 million elderly (people above age 60 years)—about 8.5% of the population. These numbers are based on the 2011 census[2]. In 2018, the government stated in the Parliament that India will have 34 crore people above 60 years of age by 2050, which would be more than the total population of the United States of America[3].

Advancing age is a risk factor for the development of malignancy, with persons over 65 years accounting for 60% of newly diagnosed malignancies and 70% of all cancer deaths, mainly cancers of the breast, lung, prostate, cervix, esophagus, and ovary[4]. It has been predicted that in 2026, in India, there will be approximately 4,50,000 men and 3,70,000 women with cancer in the ≥60 years age group[5].

Management of the older patient with cancer is a challenge. These patients have comorbidities, are on multiple medications, are often frail, and may have social, economic and psychological problems. Many times, the older patients with cancer are not considered for a curative treatment approach, despite the tumor being amenable to radical treatment, just by virtue of their age. Even when these geriatric oncology patients are treated with chemotherapy, radiation and/or surgery, they may experience more toxicity, and higher chance of morbidity and mortality. Some fit older patients may be denied aggressive therapy for fear of excessive morbidity and some frail older patients may receive therapy that is overly aggressive, leading to morbidity. Striking the right balance is a challenge. Many of the landmark clinical trials have excluded geriatric oncology patients, making the management decision even more difficult. The American Society of Clinical Oncology (ASCO)[6] and the International Society of Geriatric Oncology (SIOG)[7] have official guidelines that detail the recommended evaluation of an older patient with cancer, and include evaluation for various vulnerabilities, a thorough check of the medications and assessment for the presence of polypharmacy or the presence of potentially inappropriate medications, assessment of the risk of chemotherapy toxicity and evaluation of life expectancy. This comprehensive evaluation can be time and resource-consuming and most often, our older patients with cancer at Tata Memorial Hospital are evaluated and treated based on clinician discretion without the use of a comprehensive geriatric assessment and without the use of the various standard validated tools.

In order to improve the care of our older patients with cancer, we started the geriatric oncology clinic at Tata Memorial Center on 15 June 2018. We would like to, retrospectively and prospectively, analyze the data gathered in this geriatric oncology clinic.


  Aims anda Objectives: Top


Primary objectives: To describe the profile of the patients evaluated at the geriatric oncology clinic at Tata Memorial Hospital. This description will include the following:

  1. The incidence of vulnerabilities in the domains of function, falls, comorbidities, social, psychological, cognition and nutrition.
  2. The incidence of polypharmacy and of potentially inappropriate medications.


Secondary objectives: To describe the following:

  1. Time taken to perform the comprehensive geriatric assessment.
  2. Acceptability and feasibility of the various self-administered questionnaires for our patients.
  3. To assess if various short screening tools like G8, VAS, and TRST can predict the presence of an abnormality in the comprehensive geriatric assessment.
  4. The chemotherapy risk assessment profile of the patients.
  5. Quality of life of geriatric oncology patients and caregivers.
  6. The prevalence of fatigue, falls, insomnia, constipation, urinary obstruction and various other symptoms in geriatric oncology patients.
  7. To evaluate whether the vulnerabilities of the geriatric oncology patients were recognized and/or addressed during the preceding routine oncology assessment and workup, prior to referral to the geriatric oncology clinic.
  8. The prevalence of anemia, hypoalbuminemia, renal dysfunction, hyponatremia, and various other laboratory abnormalities.
  9. The ability of the chemotherapy risk assessment calculator to predict actual therapy toxicity in the patients who went on to receive systemic therapy.
  10. To evaluate the correlation of the presence of vulnerabilities in the various domains to the development of chemotherapy toxicity.
  11. To evaluate the progression-free survival (PFS) and overall survival (OS) in our geriatric oncology patients.
  12. To evaluate the correlation of the presence of vulnerabilities to overall survival.
  13. To evaluate the correlation of the presence of polypharmacy and/or potentially inappropriate medications to the development of chemotherapy toxicity and/or overall survival.
  14. To evaluate if any other factors like social factors, laboratory abnormalities, type of primary, tumor stage, presence of various symptoms like fatigue, etc., may impact on the overall survival.


Inclusion Criteria: The inclusion criteria for evaluation in the geriatric oncology clinic were as follows:

  1. Age 60 years and over
  2. Diagnosis of malignancy
  3. Eastern Cooperative Oncology Group (ECOG) performance status 0 to 3, i.e., patient not completely disabled.


For the retrospective analysis, we included all patients whose details had been entered in the prospectively maintained database of the geriatric oncology clinic from 15 June 2018 onwards, until date.


  Exclusion Criteria Top


The only exclusion criterion for evaluation in the geriatric oncology clinic was patient or caregiver refusal to undergo assessment.


  Methods Top


The geriatric oncology clinic was started on 15 June 2018 and was held once a week in the outpatient department of medical oncology at Tata Memorial Hospital, Parel, Mumbai. The study was divided into two portions: the retrospective portion consisted of the details of the patients who had already undergone evaluation in the geriatric oncology clinic. Once the IEC granted approval, the second portion of the study, i.e., the prospective portion, started in which patients were prospectively evaluated and the data was analyzed after obtaining written informed consent.

Patients underwent a comprehensive geriatric assessment and the information was prospectively entered into a Microsoft Excel sheet. For the first portion of the study, there was no written informed consent for being evaluated in the geriatric clinic, however, verbal consent was taken. Once IEC approval was obtained, the second prospective portion of the study began, and the data was collected prospectively in a database, after obtaining written informed consent.

Process of geriatric assessment: Patients were explained the need for a geriatric assessment and were asked if they were willing to proceed. The assessment was carried out by one to three (depending on availability) medical oncologists with the help of a social worker. We followed the ASCO and SIOG guidelines for comprehensive geriatric assessment (CGA).[6],[7] Patients who were willing were interviewed, asked to fill out the various questionnaires (help was provided by the oncologist or the social worker, if necessary), timed get up and go was checked in the clinic room and height, weight, mid-arm and mid-thigh circumferences were measured. Disease-related and treatment-related information were obtained from the electronic medical records. Patients filled out the QOL form (EORTC QLQ C30) and caregivers who were willing filled out the Caregiver Burden Scale. No additional testing, either blood tests or imaging, was done. The details of the results of the geriatric assessment were made available to the patient's treating oncologists through the electronic medical records along with recommendations for possible interventions to tackle vulnerabilities in non-oncologic problem areas. In patients who were noted to have deficits in functions or history of falls, referral was made to the physiotherapy and occupational therapy department. Similarly, patients with deficits in nutrition or, depression or anxiety were referred to the dietitian and to the psychiatrist or counsellor, respectively. No change in oncologic management of the patient was made. The time taken for the assessment was recorded; however this did not include the time spent by the patient filling out the self-administered questionnaires for ADL, IADL, GDS-Short Form, GAD-7, OARS-MSS or MOS-SSS, and QOL forms and the time spent assessing the MNA by the social worker.

Domains assessed: We recorded the patient's demographic details (age, gender, education, address, living situation and number of caregivers, profession, smoking history) and disease- related features (primary tumor, stage, intent of therapy and therapy planned). We documented the medications that the patient was taking, both prescription and over-the- counter, including the use of alternative medicines (Ayurvedic/Homeopathic/Naturopathic/other), and the use of potentially inappropriate medicines as per Beer's criteria[8]. We asked the patients about the presence of symptoms like insomnia, constipation, falls, fatigue (fatigue was quantified using the MOB-T and MOB-H scales)[9], urinary incontinence, acidity or gastric ulcers, and the presence of sensory deficits like vision or hearing impairment.

Not all domains were assessed in all patients. Our understanding of the process of a comprehensive geriatric assessment evolved with time and we added or changed assessments. We assessed the following domains:

  1. Function: We documented the ECOG performance status, ADL (Katz Index of Independence in Activities of Daily Living)[10], IADL (Lawton Scale)[11], and one performance-based measure of mobility (Timed Up and Go test, TUG, in which the patient was asked to sit in a chair; the timer was started and the patient was asked to get up, walk a distance of three meters, turn around, walk back and sit down on the chair again, at which point the timer was stopped)[12].
  2. Falls: We asked the patient if they had experienced a fall(s) in the preceding year.
  3. Nutrition: We used the height and weight to calculate the body mass index (BMI: weight in kilograms divided by the height in meters-squared) and we asked whether the patient had experienced any unintentional weight loss. We realized that several patients had a low BMI without unintentional weight loss, perhaps reflecting that some Indians may be constitutionally thinner than global standards. Several other patients had never weighed themselves and were unable to quantify the degree of weight loss. We therefore started administering the MNA as well, for which we measured the mid-arm and the mid-thigh circumference with a measuring tape[13].
  4. Psychological: We screened for the presence of depression with the GDS-Short Form[14] and for the presence of anxiety with the GAD-7[15].
  5. Comorbidities: These were assessed using the Charlson Comorbidity Index (CCI)[16] and the Cumulative Illness Rating Scale for Geriatrics (CIRS-G)[17].
  6. Cognition: We used the full Mini-Mental State Examination (MMSE)[18]. Although this took a longer time than the short cognitive screening tools like the Mini-Cog Test[19] and Blessed Orientation-Memory-Concentration (BOMC) Test[20], early on we found that these were not culturally appropriate in the Indian setting. Many of the patients did not know how to read, or how to draw time on a clock and many patients did not know the exact time, or the months of the year as per the Gregorian calendar.
  7. Social: We recorded the living situation of the patient, how many persons they were living with, how many caregivers were available, and who exactly the caregivers were. We evaluated the social support with the use of OARS-MSS[21] or the RAND Medical Outcomes Study Social Support Survey Resources (MOS-SSS)[22].
  8. Screening tools: We scored the patients using the G8 questionnaire[23], VES-13[24] and the Triage Risk Screening Tool (TRST)[25] in an attempt to assess whether these screening tools could reliably predict a deficit in the comprehensive geriatric assessment in our Indian patients.


Scoring of the geriatric scales: We used the standard scoring systems described with the tools [Table 1].



Chemotherapy risk assessment: We used either the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) tool[26] or the Cancer and Aging Research Group (CARG) online toxicity tool[27],[28] to assess the chemotherapy toxicity risk for the patients. CARG score risk groups were per the derivation study: low risk (score 0–5), intermediate risk (score 6–9), and high risk (score ≥10).

Determination of non-cancer life expectancy: We used the ePrognosis website to determine the Lee and the Schonberg index for each patient[29].

Quality of life: From July 2019 onwards, we requested patients to fill out QoL forms (European Organisation for Research and Treatment of Cancer QLQ-C30, v. 3.0)[30]. The QLQ-C30 questionnaire consists of thirty questions, including five multi-item functioning scales (physical, role, social, emotional, and cognitive functioning), nine symptom scales (pain, fatigue, nausea/vomiting, constipation, sleep, appetite, dyspnea, and financial impact) and two items that measure overall health/QoL. We asked the patient to identify their primary caregiver, and we requested that caregiver to fill out the Caregiver Burden Scale, if they were willing. The Caregiver Burden Scale consists of a 22-item self-administered questionnaire that assesses the experience of the caregiver in terms of caring for the patient. Each question has five possible answers ranging from never (0 points) to nearly always (4 points). Total score ranges from 0 to 88, with a score of 0 to 20 signifying little or no burden; 21 to 40 signifying mild to moderate burden; 41 to 60 moderate to severe burden; 61 to 88 signifying severe burden[31].


  Statistical Analysis Top


Sample size:

As this was a retrospective and prospective analysis, no calculation for sample size was done. For the analysis, we included all patients who had been included in the geriatric oncology database.

Statistical analysis

Data were prospectively entered in a Microsoft Excel database. Data will be entered into SPSS v. 20 for the purpose of analysis. Demographics, clinical details, deficits in the various domains, polypharmacy, and the presence of potentially inappropriate medications were presented with descriptive statistics, using absolute numbers, simple percentages, median, range, and interquartile range (IQR). If even a single test was used to assess a particular domain, then that domain was considered to have been tested in that patient. To calculate the proportion of patients with a deficit in a particular domain, all the patients in whom that domain was tested were taken as the denominator.

To evaluate the screening tests-G8, VES and TRST, we calculated the sensitivity, specificity, positive predictive value, negative predictive value (NPV), likelihood ratio of a positive and negative test, overall accuracy, and area under the receiver operating characteristic curve (ROC AUC) for each screening tool.

For toxicity assessment, the details of toxicity of chemotherapy was captured from the electronic medical record and the patient's clinical file and was scored according to the common toxicity criteria for adverse events (CTCAE), v. 5. Simple percentages were used to describe toxicity. Association between the CARG chemotherapy risk score and the development of severe toxicity was tested using Chi-squared test of association. Univariate and multivariate logistic regression were used to explore potential associations between toxicity and covariates, with the CARG score treated as a continuous variable.

For evaluation of PFS (calculated as the date of diagnosis to date of disease progression either objectively on scan or date of symptomatic deterioration in the absence of objective evidence of PD or date of death from any cause) and OS (calculated as the date of diagnosis to date of death from any cause), survival analysis was done using Kaplan–Meier method. To evaluate the factors that affected survival and toxicity, log rank test and Cox proportional-hazards model were used.

The mean scores and standard deviations of HR QoL scores were calculated according to the EORTC QLQ scoring manual and using percentages, means, standard deviations, t test, and Chi-squared test.


  Ethical Considerations Top


This is a standard assessment of patients who have undergone routine comprehensive geriatric assessment. Once IEC approval was obtained, we included patients after obtaining written informed consent. Since the geriatric evaluation was part of routine care, there were no ethical considerations. At the time of analysis and publication, the patient data was anonymized, and no form of patient identity was revealed.


  References Top


  1. Available online at: https://www.nih.gov/news-events/news-releases/worlds-older- population-grows-dramatically. Last accessed on 28th Nov 2019
  2. Available online at: https://www.indiatoday.in/magazine/nation/story/20180507- branded-corporate-elderly-care-old-age-homes-1221657-2018-04-26. Last accessed on 28th Nov 2019.
  3. Available online at: https://economictimes.indiatimes.com/news/politics-and- nation/demographic-time-bomb-young-india-ageing-much-faster-than- expected/articleshow/65382889.cms?from=mdr. Last accessed on 28th Nov 2019.
  4. Available online at: https://www.cancer.org/content/dam/cancer-org/research/cancer- facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures- 2019.pdf. Last accessed on 28th Nov 2019.
  5. Yeole BB, Kurkure AP, Koyande SS. Geriatric cancers in India: an epidemiological and demographic overview. Asian Pac J Cancer Prev. 2008 Apr-Jun; 9(2):271-4.
  6. Mohile SG, Dale W, Somerfield MR, Schonberg MA, Boyd CM, Burhenn PS, et al. Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Chemotherapy: ASCO Guideline for Geriatric Oncology. J Clin Oncol. 2018;36(22):2326-2347.
  7. Wildiers H, Heeren P, Puts M, Topinkova E, Janssen-Heijnen ML, Extermann M, et al. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer.J Clin Oncol. 2014 Aug 20;32(24):2595-603.
  8. By the 2019 American Geriatrics Society Beers Criteria® Update Expert Panel.American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. 2019;67(4):674-694.
  9. Avlund K, Holstein BE. Functional ability among elderly people in three service settings: the discriminatory power of a new functional ability scale. Eur J Epidemiol. 1998 Dec; 14(8):783-90.
  10. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged; The Index of ADL: A standardized measure of biological and psychosocial function. JAMA. 1963;185:914-9.
  11. Lawton MP, Brody EM. Assessment of older people: self-monitoring and instrumental activities of daily living. Gerontologist. 1969;9:179-86.
  12. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther. 2000;80(9):896-903.
  13. Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, et al. The Mini Nutritional Assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999;15(2):116-22.
  14. Almeida OP, Almeida SA. Short versions of the geriatric depression scale: a study of their validity for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV. Int J Geriatr Psychiatry. 1999;14(10):858-65.
  15. Spitzer RL, Kroenke K, Williams JBW, Lowe B. A brief measure for assessing generalized anxiety disorder. Arch Inern Med. 2006;166:1092-1097.
  16. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83.
  17. Wedding U, Roehrig B, Klippstein A, Steiner P, Schaeffer T, Pientka L, et al. Comorbidity in patients with cancer: prevalence and severity measured by cumulative illness rating scale.Crit Rev Oncol Hematol. 2007 Mar; 61(3):269-76.
  18. Pangman VC, Sloan J, Guse L.An examination of psychometric properties of the mini-mental state examination and the standardized mini-mental state examination: implications for clinical practice. Appl Nurs Res. 2000;13(4):209-13.
  19. Borson S, Scanlan JM, Chen P, Ganguli M. The Mini-Cog as a screen for dementia: validation in a population-based sample. J Am Geriatr Soc. 2003;51(10):1451-4.
  20. Fillenbaum GG, Heyman A, Wilkinson WE, Haynes CS. Comparison of two screening tests in Alzheimer's disease. The correlation and reliability of the Mini- Mental State Examination and the modified Blessed test. Arch Neurol. 1987;44(9):924-7.
  21. Burholt V, Windle G, Ferring D, Balducci C, Fagerström C, Thissen F, et al. Reliability and validity of the Older Americans Resources and Services (OARS) social resources scale in six European countries. J Gerontol B Psychol Sci Soc Sci. 2007 Nov; 62 (6):S371-9.
  22. Kornblith AB, Herndon JE 2nd, Zuckerman E, Viscoli CM, Horwitz RI, Cooper MR, Harris L, Tkaczuk KH, Perry MC, Budman D, Norton LC, Holland J; Cancer and Leukemia Group B. Social support as a buffer to the psychological impact of stressful life events in women with breast cancer. Cancer. 2001 Jan 15;91(2):443-54.
  23. Agemi Y, ShimokawaT, Sasaki J, Miyazaki K, Misumi Y, Sato A, et al. Prospective evaluation of the G8 screening tool for prognostication of survival in elderly patients with lung cancer: A single-institution study. PLoS One. 2019;14(1):e0210499.
  24. Joshi A, Tandon N, Patil VM, Noronha V, Gupta S, Bhattacharjee A, et al. Agreement analysis between three different short geriatric screening scales in patients undergoing chemotherapy for solid tumors. J Can Res Ther2017;13:1023-6.
  25. Lee JS, Schwindt G, Langevin M, Moghabghab R, Alibhai SM, Kiss A, et al. Validation of the triage risk stratification tool to identify older persons at risk for hospital admission and returning to the emergency department. J Am Geriatr Soc. 2008;56 (11):2112-7.
  26. Extermann M, Boler I, Reich RR, Lyman GH, Brown RH, DeFelice J, et al. Predicting the risk of chemotherapy toxicity in older patients: the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) score. Cancer. 2012;118(13):3377- 86.
  27. Hurria A, Togawa K, Mohile SG, Owusu C, Klepin HD, Gross CP, et al. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J.
  28. Available online at: http://www.mycarg.org/Chemo_Toxicity_Calculator. Accessed on 2nd Nov 2019.
  29. Available online at: https://eprognosis.ucsf.edu/leeschonberg.php. Accessed on 2nd Nov 2019.
  30. Available online at: https://www.eortc.org/app/uploads/sites/2/2018/08/Specimen- QLQ-C30-English.pdf. Accessed on 2nd Nov 2019.
  31. Zetit SH, Reever KE, Bach-Peterson 1. Relatives of the impaired elderly: correlates of feelings of burden. Gerontologist 1980;20:649-55.




 
  References Top

1.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424.  Back to cited text no. 1
    
2.
Comparetto C, Borruto F. Cervical cancer screening: A never-ending developing program. World J Clin Cases 2015;3:614–24.  Back to cited text no. 2
    
3.
Bretthauer M, Kalager M. Principles, effectiveness and caveats in screening for cancer. BJS Br J Surg 2013;100:55–65.  Back to cited text no. 3
    
4.
Cancer Facts & Figures 2022 | American Cancer Society. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html. [Last accessed on 2022 Feb 02].  Back to cited text no. 4
    
5.
Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146-56.  Back to cited text no. 5
    
6.
Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of Aging. ScientificWorldJournal 2001;1:323–36.  Back to cited text no. 6
    
7.
Noronha V, Talreja V, Joshi A, Patil V, Prabhash K. Survey for geriatric assessment in practicing oncologists in India. Cancer Res Stat Treat 2019;2:232.  Back to cited text no. 7
  [Full text]  
8.
Podsiadlo D, Richardson S. The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991;39:142–8.  Back to cited text no. 8
    
9.
Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther 2000;80:896–903.  Back to cited text no. 9
    
10.
Vetrano DL, Rizzuto D, Calderón-Larrañaga A, Onder G, Welmer A-K, Qiu C, et al. Walking speed drives the prognosis of older adults with cardiovascular and neuropsychiatric multimorbidity. Am J Med 2019;132:1207-15.e6.  Back to cited text no. 10
    
11.
Chun S, Shin DW, Han K, Jung JH, Kim B, Jung H-W, et al. The Timed Up and Go test and the ageing heart: Findings from a national health screening of 1,084,875 community-dwelling older adults. Eur J Prev Cardiol 2021;28:213–9.  Back to cited text no. 11
    
12.
Chua KY, Lim WS, Lin X, Yuan J-M, Koh W-P. Handgrip Strength and Timed Up-and-Go (TUG) test are predictors of short-term mortality among elderly in a population-based cohort in Singapore. J Nutr Health Aging 2020;24:371–8.  Back to cited text no. 12
    
13.
Hurria A, Togawa K, Mohile SG, Owusu C, Klepin HD, Gross CP, et al. Predicting chemotherapy toxicity in older adults with cancer: A prospective multicenter study. J Clin Oncol 2011;29:3457–65.  Back to cited text no. 13
    
14.
Blackwood J, Rybicki K. Assessment of gait speed and timed up and go measures as predictors of falls in older breast cancer survivors. Integr Cancer Ther 2021;20:15347354211006462.  Back to cited text no. 14
    
15.
Hoppe S, Rainfray M, Fonck M, Hoppenreys L, Blanc J-F, Ceccaldi J, et al. Functional decline in older patients with cancer receiving first-line chemotherapy. J Clin Oncol 2013;31:3877–82.  Back to cited text no. 15
    
16.
Huisman MG, van Leeuwen BL, Ugolini G, Montroni I, Spiliotis J, Stabilini C, et al. “Timed Up & Go”: A screening tool for predicting 30-day morbidity in onco-geriatric surgical patients? A multicenter cohort study. PLoS One 2014;9:e0086863.  Back to cited text no. 16
    
17.
Soubeyran P, Fonck M, Blanc-Bisson C, Blanc J-F, Ceccaldi J, Mertens C, et al. Predictors of early death risk in older patients treated with first-line chemotherapy for cancer. J Clin Oncol Off J Am Soc Clin Oncol 2012;30:1829–34.  Back to cited text no. 17
    
18.
Noronha V, Ramaswamy A, Banavali S, Gattani S, Prabhash K. Ethnocultural inequity in the geriatric assessment. Cancer Res Stat Treat 2020;3:808-13.  Back to cited text no. 18
  [Full text]  
19.
Noronha V, Ramaswamy A, Dhekle R, Talreja V, Gota V, Gawit K, et al. Initial experience of a geriatric oncology clinic in a tertiary cancer center in India. Cancer Res Stat Treat 2020;3:208-17.  Back to cited text no. 19
  [Full text]  
20.
Hurria A, Mohile S, Gajra A, Klepin H, Muss H, Chapman A, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016;34:2366–71.  Back to cited text no. 20
    
21.
Mohile SG, Dale W, Somerfield MR, Schonberg MA, Boyd CM, Burhenn PS, et al. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol 2018;36:2326-47.  Back to cited text no. 21
    
22.
Verweij NM, Schiphorst AH, Pronk A, van den Bos F, Hamaker ME. Physical performance measures for predicting outcome in cancer patients: A systematic review. Acta Oncol Stockh Swed 2016;55:1386–91.  Back to cited text no. 22
    
23.
Gunasekaran V, Subramanian MS, Singh V, Dey AB. Outcome of older adults at risk of frailty. AGING Med 2021;4:266–71.  Back to cited text no. 23
    
24.
Biesma B, Wymenga AN, Vincent A, Dalesio O, Smit HJ, Stigt JA, et al. Quality of life, geriatric assessment and survival in elderly patients with non-small-cell lung cancer treated with carboplatin-gemcitabine or carboplatin-paclitaxel: NVALT-3 a phase III study. Ann Oncol Off J Eur Soc Med Oncol 2011;22:1520–7.  Back to cited text no. 24
    
25.
Hamaker ME, Mitrovic M, Stauder R. The G8 screening tool detects relevant geriatric impairments and predicts survival in elderly patients with a haematological malignancy. Ann Hematol 2014;93:1031–40.  Back to cited text no. 25
    
26.
Szumacher E, Sattar S, Neve M, Do K, Ayala AP, Gray M, et al. Use of comprehensive geriatric assessment and geriatric screening for older adults in the radiation oncology setting: A systematic review. Clin Oncol R Coll Radiol G B 2018;30:578–88.  Back to cited text no. 26
    
27.
Kanesvaran R, Li H, Koo K-N, Poon D. Analysis of prognostic factors of comprehensive geriatric assessment and development of a clinical scoring system in elderly Asian patients with cancer. J Clin Oncol Off J Am Soc Clin Oncol 2011;29:3620–7.  Back to cited text no. 27
    
28.
Deschler B, Ihorst G, Platzbecker U, Germing U, März E, de Figuerido M, et al. Parameters detected by geriatric and quality of life assessment in 195 older patients with myelodysplastic syndromes and acute myeloid leukemia are highly predictive for outcome. Haematologica 2013;98:208–16.  Back to cited text no. 28
    
29.
Barry E, Galvin R, Keogh C, Horgan F, Fahey T. Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: A systematic review and meta- analysis. BMC Geriatr 2014;14:14.  Back to cited text no. 29
    
30.
Gunasekaran V, Banerjee J, Dwivedi SN, Upadhyay AD, Chatterjee P, Dey AB. Normal gait speed, grip strength and thirty seconds chair stand test among older Indians. Arch Gerontol Geriatr 2016;67:171–8.  Back to cited text no. 30
    
31.
Subramanian MS, Singh V, Chatterjee P, Dwivedi SN, Dey AB. Prevalence and predictors of falls in a health-seeking older population: An outpatient-based study. AGING Med 2020;3:28–34.  Back to cited text no. 31
    
32.
den Ouden ME, Schuurmans MJ, Arts IE, van der Schouw YT. Physical performance characteristics related to disability in older persons: A systematic review. Maturitas 2011;69:208–19.  Back to cited text no. 32
    
33.
Tangen GG, Robinson HS. Measuring physical performance in highly active older adults: Associations with age and gender? Aging Clin Exp Res 2020;32:229–37.  Back to cited text no. 33
    
34.
Ibrahim A, Singh DK, Shahar S. 'Timed Up and Go' test: Age, gender and cognitive impairment stratified normative values of older adults. PLoS One 2017;12:e0185641.  Back to cited text no. 34
    
35.
Svinøy O-E, Hilde G, Bergland A, Strand BH. Timed Up and Go: Reference values for community-dwelling older adults with and without arthritis and non-communicable diseases: The Tromsø study. Clin Interv Aging 2021;16:335–43.  Back to cited text no. 35
    
36.
Bergland A, Jørgensen L, Emaus N, Strand BH. Mobility as a predictor of all-cause mortality in older men and women: 11.8 year follow-up in the Tromsø study. BMC Health Serv Res 2017;17:22.  Back to cited text no. 36
    
37.
Rozzini R, Frisoni GB, Bianchetti A, Zanetti O, Trabucchi M. Physical performance test and activities of daily living scales in the assessment of health status in elderly people. J Am Geriatr Soc 1993;41:1109–13.  Back to cited text no. 37
    
38.
Cesari M, Onder G, Russo A, Zamboni V, Barillaro C, Ferrucci L, et al. Comorbidity and physical function: Results from the aging and longevity study in the Sirente geographic area (ilSIRENTE study). Gerontology 2006;52:24–32.  Back to cited text no. 38
    
39.
Nevill A, Duncan M, Cheung DS, Wong AS, Kwan RY, Lai CK. The use of functional performance tests and simple anthropomorphic measures to screen for comorbidity in primary care. Int J Older People Nurs 2020;15:e12333.  Back to cited text no. 39
    
40.
Al Haddad MA, John M, Hussain S, Bolton CE. Role of the timed up and go test in patients with chronic obstructive pulmonary disease. J Cardiopulm Rehabil Prev 2016;36:49–55.  Back to cited text no. 40
    
41.
Son KY, Shin DW, Lee JE, Kim SH, Yun JM, Cho B. Association of timed up and go test outcomes with future incidence of cardiovascular disease and mortality in adults aged 66 years: Korean national representative longitudinal study over 5.7 years. BMC Geriatr 2020;20:111.  Back to cited text no. 41
    
42.
Clouston SA, Brewster P, Kuh D, Richards M, Cooper R, Hardy R, et al. The dynamic relationship between physical function and cognition in longitudinal aging cohorts. Epidemiol Rev 2013;35:33–50.  Back to cited text no. 42
    
43.
Chhetri JK, Chan P, Vellas B, Cesari M. Motoric cognitive risk syndrome: Predictor of dementia and age-related negative outcomes. Front Med 2017;4. Available from: https://www.frontiersin.org/article/10.3389/fmed. 2017.00166. [Last accessed on 2022 Feb 08].  Back to cited text no. 43
    
44.
Foley MP, Hasson SM. Effects of a community-based multimodal exercise program on health-related physical fitness and physical function in breast cancer survivors: A pilot study. Integr Cancer Ther 2016;15:446–54.  Back to cited text no. 44
    
45.
Almstedt HC, Grote S, Perez SE, Shoepe TC, Strand SL, Tarleton HP. Training-related improvements in musculoskeletal health and balance: A 13-week pilot study of female cancer survivors. Eur J Cancer Care (Engl) 2017;26. doi: 10.1111/ecc. 12442.  Back to cited text no. 45
    
46.
Morishita S, Hamaue Y, Fukushima T, Tanaka T, Fu JB, Nakano J. Effect of exercise on mortality and recurrence in patients with cancer: A systematic review and meta-analysis. Integr Cancer Ther 2020;19:1534735420917462.  Back to cited text no. 46
    
47.
Takemura N, Chan SL, Smith R, Cheung DS, Lin C-C. The effects of physical activity on overall survival among advanced cancer patients: A systematic review and meta-analysis. BMC Cancer 2021;21:242.  Back to cited text no. 47
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  Supplementary Ap...Aims anda Object...Ethical Consider...
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusion
Exclusion Criteria
Methods
Statistical Analysis
References
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed430    
    Printed10    
    Emailed0    
    PDF Downloaded32    
    Comments [Add]    

Recommend this journal