The aim of this study was to establish ‘Timed up and Go’ test (TUG) normative data among community dwelling older adults stratified based on cognitive status, gender and age groups. A total of 2084 community dwelling older adults from wave I and II were recruited through a multistage random sampling method. TUG was performed using the standard protocol and scores were then stratified based on with and without mild cognitive impairment (MCI), gender and in a 5-year age groups ranging from ages of 60’s to 80’s. 529(16%) participants were identified to have MCI. Past history of falls and medical history of hypertension, heart disease, joint pain, hearing and vision problem, and urinary incontinence were found to have influenced TUG performance. Cognitive status as a mediator, predicted TUG performance even when both gender and age were controlled for (B 0.24, 95% CI (0.02–0.47), β 0.03, t 2.10, p = 0.36). Further descriptive analysis showed, participants with MCI, women and older in age took a longer time to complete TUG, as compared to men with MCI across all age groups with exceptions for some age groups. These results suggested that MCI needs to be taken into consideration when testing older adults using TUG, besides age and gender factors. Data using fast speed TUG may be required among older adults with and without MCI for further understanding.

Purpose: This study was aimed at determining the presence of cognitive frailty and its associated factors among community-dwelling older adults from the “LRGS-Towards Useful Aging (TUA)” longitudinal study. Patients and methods: The available data related to cognitive frailty among a sub-sample of older adults aged 60 years and above (n=815) from two states in Malaysia were analysed. In the LRGS-TUA study, a comprehensive interview-based questionnaire was administered to obtain the socio-demographic information of the participants, followed by assessments to examine the cognitive function, functional status, dietary intake, lifestyle, psychosocial status and biomarkers associated with cognitive frailty. The factors associated with cognitive frailty were assessed using a bivariate logistic regression (BLR). Results: The majority of the older adults were categorized as robust (68.4%), followed by cognitively pre-frail (37.4%) and cognitively frail (2.2%). The data on the cognitively frail and pre-frail groups were combined for comparison with the robust group. A hierarchical BLR indicated that advancing age (OR=1.04, 95% CI:1.01–1.08, p<0.05) and depression (OR=1.49, 95% CI:1.34–1.65, p<0.001) scored lower on the Activity of Daily Living (ADL) scale (OR=0.98, 95% CI:0.96–0.99, p<0.05), while low social support (OR=0.98, 95% CI:0.97–0.99, p<0.05) and low niacin intake (OR=0.94, 95% CI:0.89–0.99, p<0.05) were found to be significant factors for cognitive frailty. Higher oxidative stress (MDA) and lower telomerase activity were also associated with cognitive frailty (p<0.05). Conclusion: Older age, a lower niacin intake, lack of social support, depression and lower functional status were identified as significant factors associated with cognitive frailty among older Malaysian adults. MDA and telomerase activity can be used as potential biomarkers for the identification of cognitive frailty.

Background: Cognitive frailty, a combination of physical frailty and cognitive impairment, is associated with functional decline in older adults. However, there is limited information if cognitive frailty predicts the incidence of falls, injuries, and disability. In this study, we aimed to determine the ability of cognitive frailty in predicting the incidence of falls, injuries and disability among multi-ethnic older adults in Malaysia at 5 years follow-up. Methods: In this prospective cohort study, a total of 400 participants aged 60 years and above were successfully followed up at 5 years. Participants’ socio-demographic, medical history, psycho-social, physical, cognitive and dietary intake information was obtained. Cognitive frailty was defined as comorbid physical frailty (> 1 Fried criteria) and mild cognitive impairment (Petersen criteria). Univariate analysis was performed for all variables, followed by hierarchical binary logistic regression (BLR) analysis to identify the ability of CF in predicting the incidence of falls, injuries, and disability. The significant value was set at p < 0.05. Results: Cognitive frailty was found to be associated with greater risk of adverse consequences after adjusting for covariates. Both cognitive frailty (Adjusted Odd ratio (Adj OR) = 2.98, 95% confidence interval (CI): 1.78–4.99, p < 0.05) and physical frailty (Adj OR = 2.88, 95% CI: 1.19–6.99, p < 0.05) were significant predictors of incidence of falls. Risk of injuries was also significantly increased with the presence of cognitive frailty (Adj OR = 3.06, 95% CI: 1.23–7.60, p < 0.05) and physical frailty (Adj OR = 3.04, 95% CI: 1.75–5.28, p < 0.05). In addition, cognitive frailty (Adj OR = 5.17, 95% CI: 1.11–24.21, p < 0.05) and physical frailty (Adj OR = 4.99, 95% CI: 1.11–22.57, p < 0.05) were shown to significantly predict the incidence of disability among older adults. Conclusion: Cognitive frailty is a robust predictor of falls, injuries, and disability in older adults. Possible early multi-domain preventive and management strategies of cognitive frailty that contribute to adverse consequences are required to decrease further functional decline and promote independence in older adults.

Poverty at old age is associated with poor dietary habit, nutritional status and higher rates of chronic diseases and psychosocial problems. However, there is limited information about this matter according to urban and rural settings. The aim of this study was to identify dietary, nutritional, physical and cognitive factors associated with poor socioeconomic status (SES) among older adults according to urban and rural settings in Malaysia. An analysis was conducted among 2237 older adults who participated in a longitudinal study on aging (LRGS TUA). This study involved four states in Malaysia, with 49.4% from urban areas. Respondents were divided into three categories of SES based on percentile, stratified according to urban and rural settings. SES was measured using household income. The prevalence of low SES was higher among older adults in the rural area (50.6%) as compared to the urban area (49.4%). Factors associated with low SES among older adults in an urban setting were low dietary fibre intake (Adj OR:0.91),longer time for the Timed up and Go Test (Adj OR:1.09), greater disability (Adj OR:1.02), less frequent practice of caloric restriction (Adj OR:1.65), lower cognitive processing speed score (Adj OR:0.94) and lower protein intake (Adj OR:0.94). Whilst, among respondents from rural area, the factors associated with low SES were lack of dietary fibre intake (Adj OR:0.79), lower calf circumference (Adj OR: 0.91), lesser fresh fruits intake (Adj OR:0.91), greater disability (Adj OR:1.02) and having lower score in instrumental activities of daily living (Adj OR: 0.92). Lower SES ismore prevalent in rural areas. Poor dietary intake, lower fitness and disability were common factors associated with low in SES, regardless of settings. Factors associated with low SES identifiedin both the urban and rural areas in our study may be useful inplanning strategies to combat low SES and its related problems among older adults.

(1) Background: Cognitive frailty (CF) is the simultaneous presence of physical frailty and cognitive impairment with an increased risk of dementia. Considering that the risk factors of CF are mostly elucidated from cross-sectional studies, we conducted a community-based longitudinal study to determine the incidence and the predictors of CF among Malaysian older adults.; (2) Methods: Out of 490 older adults participating in the Malaysian Towards Useful Aging (TUA) study, 282 were successfully followed-up at five-years for an analysis of the CF incidence. CF was defined as a comorbid physical frailty (>1 Fried criteria) and mild cognitive impairment (Petersen criteria). A comprehensive interview-based questionnaire was administered for sociodemographic information, cognitive function, physical function, dietary intake, psychosocial, and biochemical indices. Univariate analyses were performed for each variable, followed by a regression analysis to identify the predictors of CF that accounted for confounding effects between the studied factors; (3) Results: The incidence rate of CF was 7.1 per 100 person-years. Advancing age (OR=1.12, 95% CI:1.04-1.21, p < 0.05), depression (OR=1.20, 95% CI:1.05-1.37, p < 0.05), decreased processing speed, assessed by a lower digit symbol score (OR=0.67, 95%CI:0.0.56-0.80, p < 0.05), decreased functional mobility measured using Timed-Up-and-Go (TUG) (OR=1.23, 95% CI:1.04-1.46, p < 0.05), low vitamin D intake (OR:0.36, 95% CI:0.14-0.93, p < 0.05) and physical frailty (OR=2.16, 95% CI:1.02-4.58, p < 0.05) were predictors for CF incidence; and (4) Conclusions: Our study results could be used as an initial reference for future studies to formulate effective preventive management and intervention strategies to decelerate CF development among older adults.

Background: Physical performance and balance declines with aging and may lead to increased risk of falls. Physical performance tests may be useful for initial fall-risk screening test among community-dwelling older adults. Physiological profile assessment (PPA), a composite falls risk assessment tool is reported to have 75% accuracy to screen for physiological falls risk. PPA correlates with Timed Up and Go (TUG) test. However, the association between many other commonly used physical performance tests and PPA is not known. The aim of the present study was to examine the association between physiological falls risk measured using PPA and a battery of physical performance tests. Methods: One hundred and forty older adults from a senior citizens club in Kuala Lumpur, Malaysia (94 females, 46 males), aged 60 years and above (65.77±4.61), participated in this cross-sectional study. Participants were screened for falls risk using PPA. A battery of physical performance tests that include ten-step test (TST), short physical performance battery (SPPB), functional reach test (FRT), static balance test (SBT), TUG, dominant hand-grip strength (DHGS), and gait speed test (GST) were also performed. Spearman’s rank correlation and binomial logistic regression were performed to examine the significantly associated independent variables (physical performance tests) with falls risk (dependent variable). Results: Approximately 13% older adults were at high risk of falls categorized using PPA. Significant differences (P<0.05) were demonstrated for age, TST, SPPB, FRT, SBT, TUG between high and low falls risk group. A significant (P<0.01) weak correlation was found between PPA and TST (r=0.25), TUG (r=0.27), SBT (r=0.23), SPPB (r=-0.33), and FRT (r=-0.23). Binary logistic regression results demonstrated that SBT measuring postural sways objectively using a balance board was the only significant predictor of physiological falls risk (P<0.05, odds ratio of 2.12). Conclusion: The reference values of physical performance tests in our study may be used as a guide for initial falls screening to categorize high and low physiological falls risk among community-dwelling older adults. A more comprehensive assessment of falls risk can be performed thereafter for more specific intervention of underlying impairments.