Volume 37 Issue 1 Article 6
2023
Development of WEFIT Screening Tool for Assessing Pre-exercise Development of WEFIT Screening Tool for Assessing Pre-exercise Fitness Level for Older Adults in Peninsular Malaysia
Fitness Level for Older Adults in Peninsular Malaysia
Divya Vanoh
Arimi Fitri Mat Ludin Suzana Shahar
Shahrul Azman Mohd Noah Zahara Abdul Manaf
Noorlaili Mohd Tohit
Follow this and additional works at: https://digital.car.chula.ac.th/jhr Part of the Medicine and Health Sciences Commons
2586-940X/© 2023 The Authors. Published by College of Public Health Sciences, Chulalongkorn University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Development of WEFIT Screening Tool for Assessing Pre-exercise Fitness Level for Older Adults in
Peninsular Malaysia
Divya Vanoh
a, Arimi F. Mat Ludin
b,*, Suzana Shahar
b, Shahrul A. Mohd Noah
c, Zahara A. Manaf
b, Noorlaili M. Tohit
daDietetics Programme, School of Health Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelatan, Malaysia
bCenter for Healthy Aging and Wellness (H-Care), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur, Malaysia
cSchool of Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
dDepartment of Family Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia
Abstract
Background:Pre-exercise screening is important to identify thefitness level of older adults prior to performing exercise.
This study aimed to develop a screening tool for identifying older adults with poor physicalfitness prior to exercising.
Method: Participants involved 2315 older adults from four states in Peninsular Malaysia. Several social and health parameters were investigated, andfitness level was assessed using hand grip strength. Receiver operating curve (ROC) analysis was chosen to determine the cut-off point, sensitivity, specificity, positive predictive value (PPV) and Youden's Index of the screening tool.
Results:Age, sex, waist circumference, hypertension, diabetes mellitus and fasting blood sugar were selected as items of screening tool. The screening tool had sensitivity of 77%, specificity of 80.9%, PPV of 79% and Youden's Index of 0.579.
Conclusion:This study provides significantfindings on the importance of having a pre-exercise screening tool which has good sensitivity, specificity, PPV and Youden's Index for identification of older adults with poor physicalfitness.
Keywords:Development, Exercise, Fitness, Older adults, Screening tool, Malaysia
1. Introduction
I
t is generally known that older people are vulnerable to various age-related changes such as functional limitation, poor cognitive function, multi- morbidities, and psychosocial problems such as depression, loneliness, and anxiety [1]. Therefore, exercise is beneficial for older people for overall satisfaction of life, reducing mental health issues such as depression and anxiety, lowering the risk of osteoporosis, heart diseases, stroke, joint pain, can- cer, diabetes, dementia and falls [2]. In addition, the World Health Organization (WHO) recommendsolder people to engage in at least 150 minutes of moderate intensity exercises per week. Chronic diseases such as osteoarthritis are often mis- interpreted by older adults as barriers to performing exercises. However, it appears that exercise acts as a pain relief and it helps in managing the severity of the diseases [3].
Furthermore, lower exercise frequency may in- crease the risk of non-communicable diseases such as diabetes and cardiovascular diseases, leading to mortality and eventually, raises the health care cost [4]. Findings from the National Health and Morbidity Survey (NHMS) in Malaysia reported
Received 22 February 2021; revised 15 July 2021; accepted 4 August 2021.
Available online 31 August 2022
*Corresponding author.
E-mail address:arimifi[email protected](A.F. Mat Ludin).
https://doi.org/10.56808/2586-940X.1006
2586-940X/©2023 College of Public Health Sciences, Chulalongkorn University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
ALARTICLE
that local older adults are physically inactive [5].
Among the factors contributing to physical inactivity in Malaysia are female, high income earners as well as the jobless, well-educated citizens, urban resi- dents, older age (age 80 years and older) and ethnicity (i.e. Bumiputra living in Sabah&Sarawak from east of Malaysia) [5]. The perceived barriers for physical activity among older adults are lack of time, health issues, lack of exercise companion, no knowledge of exercise benefits, and assumption that they arefit [6].
As older people are a vulnerable group of in- dividuals, it is always advisable for them to refer to their health-care professionals for recommended exercise types and duration according to their cur- rent medical condition. The issue is thefitness level of the older adult itself prior to performing physical activities or exercise. A specific screening tool is required to assess the fitness level of older people before exercising. Resnick and colleagues (2008) have developed the six-item Exercise Assessment and Screening for You (EASY) screening tool for determining the health status of older adults prior to exercising [7]. The EASY screening tool consists of questions regarding pain experienced while per- forming physical activity, recent experience of dizziness, previous falls or balancing problems, high blood pressure, swelling or stiffness or musculo- skeletal related issues, and any additional symp- toms hindering them from performing physical activity.
Apart from that, another commonly used exercise screening tool is the Physical Activity Readiness Questionnaire (PAR-Q) for assessing the cardio- vascular, musculoskeletal or any other obstacles preventing a person from performing physical ac- tivity. Affirmative responses to any of the questions in PAR-Q indicated the need for prior consultation with general practitioners for appropriate exercise recommendation according to their health status [8].
The need for a pre-exercise screening tool is to prevent untoward events such as injuries and falls following inappropriate exercise techniques, exer- cise modalities or over-exerting themselves. In addition, earlier detection offitness level is essential for planning and selection of personalized exercise programs to avoid exacerbation of medical conditions.
The existing screening tools are questionnaire- based and subjected to recall bias and risk of misinterpretation by older people which leads to under- or over-estimation of fitness level. In addi- tion,fitness screening prior to exercise is essential as a precautionary strategy when considering the car- diovascular risks associated with exercising such as
increased blood pressure, arrhythmia, and myocar- dial infarction [9]. Hence, it is best to identify the baseline fitness level of older adults as it is impor- tant to start an exercise at the right intensity in ensuring optimal effect. In consideration with the importance of the pre-exercise fitness assessment for older people, the current study aimed to develop an objectivefitness screening tool, known as WEFIT from a wide range of sociodemographic and health factors for rapid identification of older adults at high risk of poor physicalfitness.
2. Methodology 2.1. Recruitment
The WEFIT screening tool was developed from a secondary analysis of the baseline phase data of a three-year longitudinal study known as ‘Towards Useful Aging’, abbreviated as TUA. A total of 2315 community dwelling older adults was included in this study which was conducted in four states with the highest number of geriatric populations repre- senting each region in peninsular Malaysia namely Kelantan (east), Johor (south), Perak (north) and Selangor (central).
The multi-stage random sampling method was employed for the selection of subjects and it involved three stages namely primary sampling unit (PSU), secondary sampling unit (SSU), and tertiary sampling unit (TSU). PSU involves the selection of a state from each region, followed by the division of the state into several census circles. The census circle was further divided into living quarters. Se- lection of living quarters with at least 10% of geri- atric population was the TSU [10]. The inclusion criteria of the TUA baseline study were older adults aged 60 years and above, no dementia and not wheelchair bound. Meanwhile, the exclusion criteria were non-Malaysian citizens and those with severe health problems such as cancer with metas- tasis, stroke, AIDS or tuberculosis.
2.2. Ethical considerations
This study has been ethically approved by the Universiti Kebangsaan Malaysia Research Ethics Committee with the approval code of LRGS TUAeNNe060-2013.
2.3. Study tools
The study was conducted via an interview-based approach using a questionnaire. Parameters assessed in this study were socio-demography, self-
JOURNAL OF HEALTH RESEARCH 2023;37(1):8e14 9
ORIGINALARTICLE
reported medical history, anthropometry and physicalfitness. Socio-demography included name, age, sex, religion, living status, marital status, edu- cation years and living status. Meanwhile, co-mor- bidities assessment included self-reported hypertension, diabetes mellitus and hypercholes- terolemia. Fasting blood glucose value was obtained by withdrawing a total of 5 ml fasting venous blood by an experienced phlebotomist (Table 2). Subjects were reminded to fast overnight prior to blood withdrawal.
The blood was sent to the laboratory within 1 h of blood collection for further analysis of fasting blood
glucose value. Participants were reminded to fast overnight for the purpose of blood withdrawal. On the other hand, anthropometric measurements comprised of weight, height, body mass index (BMI), waist, calf and hip circumferences were also conducted. Physical fitness was assessed using the hand grip strength. The detailed information on the study tools used has been published elsewhere [10].
2.4. Development of WEFIT screening tool
The selection of items for the WEFIT screening tool was done in two stages. The first stage was the
Table 1. Selection of questions for WEFIT screening tool.
Question Odds Ratio (OR) P-valuea
1.) Are you a smoker? 1.525 .11
2.) What is your sex? (Men or Women) 0.054 <.001
3.) Do you feel lonely? 1.156 .15
4.) Do you stay alone or with others? 1.378 .22
5.) Do you have hypertension as diagnosed by doctor? 1.106 .59
6.) Do you have urinary incontinence? 1.216 .71
7.) What is your current waist circumference? 1.033 .02
8.) What is your current fasting blood glucose reading? 1.079 .08
9.) What is your current weight? 0.926 <.001
10.) What is your current age? 1.079 <.001
11.) Do you have diabetes mellitus as diagnosed by doctor? 1.225 .37
a Tested using Binary Logistic Regression.
Table 2. Socio-demography characteristic of the participants (N¼2315).
Parameters Men
(n¼1114)
Women (n¼1201) P-valuea
Age, mean(SD) 69.4 (6.1) 68.6±6.4 .005
Location of residence, n(%) Urban 522 (46.9) 627 (52.2) P<.001
Rural 592 (53.1) 574 (47.8)
Ethnicity, n(%) Malay Chinese Indian&Others
.02
Malay 730 (65.5) 715 (59.5)
Chinese 323 (29.0) 422 (35.1)
Indian&Others 61 (5.5) 64 (5.4)
Marital Status, n(%) Single 24 (2.2) 16 (1.3) P<.001
Married 982 (88.1) 600 (50.0)
Divorced 108 (9.7) 585 (48.7)
Education in years, mean(SD) 6.2±3.9 4.2±3.9 P<.001
Living arrangement, n(%) Alone 73 (6.6) 173 (14.4) P<.001
With others 1041 (93.4) 1028 (85.6)
Weight in kg, mean (SD) 64.4±12.0 57.5±11.8 P<.001
Waist circumference in cm, mean (SD) 89.4±10.9 87.3±11.6 P<.001
Fasting blood sugar in mmol/L, mean (SD) 6.3±2.2 6.1±2.3 P<.001
Hand grip strength in kg, mean(SD) 28.4±6.9 18.2±5.0 P<.001
Diabetes, n(%) Yes 294 (26.4) 310 (25.8) 0.90
No 820 (73.6) 891 (74.2)
Hypertension, n(%) Yes 521 (46.8) 645 (53.7) P<.001
No 593 (53.2) 556 (46.3)
Hypercholesterolemia, n(%) Yes 301 (27.0) 398 (33.1) P<.001
No 813 (73.0) 803 (66.9)
Abbreviation: SD: standard deviation.
a Numerical data analysed using independent test, while categorical using Chi-square.
ALARTICLE
univariate analysis between the parameters and the fitness level as the dependent variable. Hand grip strength was classified into two categories namely poor and good fitness levels. Categories were formed based on the median value for hand grip strength. Any participants who scored similar re- sults with or above the median value (23kg) have good fitness level. Median value was used in this study because median is not affected by extreme values and not distorted by skewed data [11]. The univariate analysis was performed using the Pear- son Chi-Square test for categorical variables and Independent-t-test for numerical variables. Factors with p-value less than 0.05 were entered into the multivariable model.
Using the hand grip strength category as the dependent variable, based on the binary logistic regression analysis, the multivariable model was formed at the second stage. Additionally, hand grip was chosen due to previous literature demon- strating its ability as the indicator of overall func- tional status and overall health [12]. The reference value for the dependent variable was the good fitness level and was coded as 0. The significant variables from the binary logistic regression model were chosen to be included as items in the WEFIT questionnaire. Besides that, certain variables such as fasting blood sugar, hypertension, diabetes which did not achieve statistical significance were still chosen to be included in the WEFIT screening tool considering their strong association with fitness level based on previous literature.
The WEFIT screening tool had six items for assessing the physicalfitness of older adults namely age, sex, waist circumference, self-reported hyper- tension, self-reported diabetes, and current fasting
blood sugar level. Users were required to measure their waist circumference using a measuring tape.
Instructions on the appropriate ways to measure waist circumference will be provided to ensure precision of waist circumference value obtained.
Waist circumference cut-off point used was based on the International Diabetes Federation; 90 cm for men and 80 cm for women [13]. Meanwhile, the fasting blood sugar reference value was from the Malaysian Clinical Practice Guidelines for Diabetes Mellitus [14]. Normal fasting blood glucose value was between 4.4 and 7.0 mmol/L. Values above 7.0 mmol/L was considered high (Table 3).
2.5. Sensitivity, specificity and predictive accuracy of the WEFIT screening tool
Youden's Index was calculated using the formula (sensitivityþspecificity)-1. The higher the Youden's Index, the more robust the screening tool. The for- mula for both sensitivity, specificity and PPV are as below:
Sensitivity: True positive/(true positive þ false negative).
Specificity: True negative/(true negative þ false positive).
PPV: True positive/(true positiveþfalse positive).
2.6. Statistical analysis
Normality test was performed using the histo- gram. Independent t-test was performed to iden- tify the mean differences between a categorical (two groups) and numerical variable. Association between two categorical variables was assessed using the Chi-Square test. On the other hand,
Table 3. Final items selected for the WEFIT screening tool.
Items Answer option
1. What is your current age? 60e75 years old
76 above
2. Gender Men
Women 3. What is your current waist circumference (in cm)?
(link of video will be provided for users to know the proper way to measure waist circumference)
Lower risk (90cm for men;80cm for women) Higher risk (90cm for men;80cm for women)
4. Do you have hypertension? No
Not sure Yes
5. Do you have diabetes mellitus? No
Not sure Yes
6. What is your current fasting blood sugar value? Low: (<4.0 mmol/L) Normal: (4.4e7.0mmol/L) High: (>7.0mmol/L)
JOURNAL OF HEALTH RESEARCH 2023;37(1):8e14 11
ORIGINALARTICLE
items for the WEFIT screening tool were identified from the binary logistic regression analysis using the hand grip strength as the dependent variable.
Area under the receiver operating characteristic curve (AUC) was determined to identify the pre- dictive accuracy of the WEFIT screening tool. The cut-off point for WEFIT was chosen based on the highest Youden's Index sensitivity, specificity and PPV values.
3. Results
3.1. Selection of questions for WEFIT screening tool
The multivariable analysis revealed that not all the questions selected for the WEFIT screening tool achieved statistical significance. However, these items had been chosen to be included in the WEFIT screening tool due tofindings from previous litera- ture showing strong association between poor fitness level with the parameters such as fasting blood sugar, hypertension, diabetes mellitus and smoking (Table 1).
3.2. Socio-demography characteristic of the participants
A total of 14.4% of older women were reported living alone. Men had higher education level, greater waist circumference, better hand grip strength and higher fasting blood sugar as compared to women (P< .001). Meanwhile, preva- lence of hypertension (53.7%) and hypercholester- olemia (33.1%) were higher in women (Table 2).
3.3. Final items selected for the WEFIT screening tool
Table 3 showed the final items selected to be incorporated into the WEFIT screening tool. It had six items with answer options. Users are required to choose one option for each of the question in the screening tool.
3.4. Scoring for items included in the WEFIT screening tool
Table 4 shows the scoring for each item in the WEFIT screening tool. The total score for WEFIT was 10, with the lowest score of 0. The fasting blood sugar value is only valid for six months. Users of this screening tool were required to have their latest blood glucose result to determine their currentfitness level.
3.5. Cut-off point WEFIT screening tool
Based onTable 5, score of 4 and higher indicated lowerfitness level. This cut-off point was chosen due to its higher sensitivity, specificity, PPV and Youden's Index. Users with scores of 4 and above are required to consult their respective doctors for advice about their exercise ability. The application will not provide spe- cific exercise recommendations for users with lower fitness levels considering the safety aspects.
4. Discussion
Pre-exercise screening tool is essential for deter- mining the initial fitness level of older individuals
Table 4. Scoring for items included in the WEFIT screening tool.
Parameters Simplified coefficient Score Distribution
Age 2 Score 0: 60-75
Score 2: 76 above
Gender 1 Men: 1
Women: 0
Waist Circumference 1 Lower risk: 0
Higher risk: 1 (90cm for men;80cm for women)
Hypertension 2 No: 0; Not sure: 1; Yes: 2
Diabetes Mellitus 2 No: 0; Not sure: 1; Yes: 2
Fasting Blood Sugar (past 6 months) 2 Low: 0 (<4.0 mmol/L); Normal: (4.4e7.0mmol/L);
High (>7.0mmol/L)
Total score 10
Table 5. Cut-off point WEFIT screening tool.
Cut-Off Sensitivity Specificity PPV AUC (95%CI) Youden's Index
≥4 77.0 80.9 79.0 85.1 (83.3e87.0) 0.579
5 76.8 80.7 78.8 85.2 (83.3e87.1) 0.575
6 76.3 80.5 78.5 85.1(83.3e87.0) 0.568
7 76.1 80.3 78.2 85.1 (83.3e87.0) 0.564
ƗPPV: positive predictive value.
ALARTICLE
before starting to exercise. Older people with po- tential health risks are recommended to seek advice from their doctors before exercising. This study has successfully developed a pre-exercise screening tool, WEFIT, consisting of six items that can be completed within short period of time which is useful in both research and clinical setting. More- over, it is a patient-centered screening tool for informing older adults of the recommended exer- cises according to their fitness levels. WEFIT screening tool is very useful tool for Malaysian older adults as it is developed using data derived from local longitudinal study. Globally, the two most common pre-exercise screening tools used among older adults are EASY and PAR-Q. EASY and PAR- Q have similarities as these were more focused more on cardiovascular issues, dizziness, musculo- skeletal or bone related problems, balance, drugs taken and other factors which limit them from exercising (7,8).
WEFIT emphasized on different items signifi- cantly related to thefitness level of Malaysian older population such as age, gender, waist circumference and underlying health problems such as hyperten- sion, diabetes and fasting blood sugar. By using WEFIT, older adults must be aware of their blood pressure, blood glucose and waist circumference which are all cardiovascular risk factors. These pa- rameters are not costly and can be obtained by conducting a health screening in the nearby gov- ernment clinic or hospitals. Users are encouraged to visit the nearby government health clinic for health screening. A study conducted among 102 Australian older adults aged 60 years and above demonstrated that age, gender and poor health are among the factors limiting the physical activity participation [15].
Gender has been chosen as one of the items in WEFIT screening tool as it is proven that there is a marked gender disparity in the physical activity levels among older adults driven by various factors namely health status, social factors, physical appearance, and types of exercises [16]. Older women are more likely to perform exercises for appealing appearance, losing weight for better health status if they are overweight or obese, and prefers activities that are less vigorous, and not competitive. On the other hand, older men have stronger preference for outdoor tasks and skill- based activities which are more challenging [16].
Thus, gender differences in exercise preference suggest that exercise recommendation should be tailored to meet the gender-specific interests for motivating older adults to engage in exercising.
Besides that, abdominal obesity represents dysfunctional adipose tissue which produces in- flammatory biomarkers such as prostaglandins, C- reactive protein, cytokines, tumor necrosis factor and leptin [17]. This may contribute to various dis- eases such as diabetes mellitus, hyperlipidemia and cardiovascular diseases. Slight reduction in adipose tissue may benefit cardiovascular health and improve metabolic function which is possible via physical activities. Exercise may improve blood flow, reduce oxidative stress, glucose tolerance and lower blood pressure [17].
Hypertension is another item assessed in the screening tool. Longitudinal studies such as the Three-City study in France and Cardiovascular Health Study in the US have demonstrated an annual decline in walking speed among the hyper- tensive older adults as compared to normotensive group [18]. Association between hypertension and poor fitness level may be due to sarcopenia [19].
Increased inflammation and oxidative stress following hypertension may trigger sarcopenia and accelerate physical disability [19].
WEFIT screening tool incorporates fasting blood sugar as one of the parameters for determining physicalfitness. Findings from the Baltimore Longi- tudinal Study of Aging demonstrated that muscle quality of hyperglycemic individuals is poorer as compared to those with normoglycemia after 7.5 years of follow-up [20]. Factors associated with low muscle quality in hyperglycemia are increased secretion of inflammatory cytokines such as interleukin-6, tumor necrosis factor-alpha and C-reactive protein [21].
The strength of this study is that it has developed a screening tool suitable for Malaysian older adults which provides individualized fitness assessment for them prior to start exercising. The use of WEFIT by other population require validation among that specific population. User centered application may motivate older adults to use it continuously. How- ever, the limitation of this study is the inability to verify the data entered by older people into this application. Inaccurate data entry leads to mis- reporting and translated into false recommendation.
Besides that, older adults who prefer to use this application are required to have a smartphone with an Internet connection.
5. Conclusion
WEFIT is a screening tool with good sensitivity, specificity and Youden's Index. Moreover, it is a simple and user-centered screening tool to inform older adults of theirfitness level. There is a potential
JOURNAL OF HEALTH RESEARCH 2023;37(1):8e14 13
ORIGINALARTICLE
to explore its development into user friendly apps for older people in the society to facilitate self- empowerment for physical activities and healthy ageing. However, older people in the society who are reluctant to use modern gadgets, can be pro- vided with informal assistance to support the use of at least smartphone health applications for contin- uous monitoring of their health status.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/
or publication of this article: This work was sup- ported by the Ministry of Higher Education Malaysia under the grant Longterm Research Grant Scheme (LRGS) [Grant number: LRGS/BU/2012/
UKMeUKM/K/01] and DCP-2017-002/3.
Conflict of interest
The authors declared that there is no conflict of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgement
We would like to thank all thefieldworks, partic- ipants, co-researchers and local authorities for their cooperation in making this study a success.
References
[1] Ramocha LM, Louw QA, Tshabalala MD. Quality of life and physical activity among older adults living in institutions compared to the community. S Afr J Physiother 2017;73(1):
342. PMID: 30135899.
[2] Low ST, Balaraman T. Physical activity level and fall risk among community-dwelling older adults. J Phys Ther Sci 2017;29(7):1121e4. PMID: 28744029.
[3] Herbolsheimer F, Schaap LA, Edwards MH, Maggi S, Otero A, Timmermans EJ, et al. Physical activity patterns among older adults with and without knee osteoarthritis in six European countries. Arthritis Care Res 2016;68(2):228e36.
PMID: 26212673.
[4] Sheikholeslami S, Ghanbarian A, Azizi F. The impact of physical activity on non-communicable diseases: findings from 20 years of the Tehran Lipid and Glucose Study. Int J Endocrinol Metabol 2018;16(4). PMID: 30584431.
[5] Kaur J, Kaur G, Ho BK, Yao WK, Salleh M, Lim KH. Pre- dictors of physical inactivity among elderly Malaysians:
recommendations for policy planning. Asia Pac J Publ Health 2015;27(3):314e22. PMID: 24425796.
[6] Ayiesah R. The level of physical activities amongst elderly in a community. JUMMEC 2007;10(1):29e33. ID: 56652782.
[7] Resnick B, Ory MG, Hora K, Rogers ME, Page P, Chodzko- Zajko W, et al. The Exercise Assessment and Screening for You (EASY) tool: application in the oldest old population.
Am J Lifestyle Med 2008;2(5):432e40.https://doi.org/10.1177/
1559827608320229.
[8] Smith ML, Ory MG, Ahn S, Bazzarre TL, Resnick B. Older adults'participation in a community-based falls prevention exercise program: relationships between the EASY tool, program attendance, and health outcomes. Gerontol 2011;
51(6):809e21. PMID: 21911846.
[9] McPhee JS, French DP, Jackson D, Nazroo J, Pendleton N, Degens H. Physical activity in older age: perspectives for healthy ageing and frailty. Biogerontology 2016;17(3):567e80.
PMID: 26936444.
[10] Manikandan S. Measures of central tendency: median and mode. J Pharmacol Pharmacother 2011;2(3):214.
[11] Bohannon RW. Grip strength: an indispensable biomarker for older adults. Clin Interv Aging 2019;14:1681.
[12] Shahar S, Omar A, Vanoh D, Hamid TA, Mukari SZ, Din NC, et al. Approaches in methodology for population-based longitudinal study on neuroprotective model for healthy longevity (TUA) among Malaysian older adults. Aging Clin Exp Res 2016;28(6):1089e104. PMID: 26670602.
[13] WHO. Waist circumference and waist-hip ratio: report of a WHO expert consultation. Geneva, Switzerland: WHO Document Production Service; 2011. ISBN: 9789241501491.
[14] Rai R, Jongenelis MI, Jackson B, Newton RU, Pettigrew S.
Factors influencing physical activity participation among older people with low activity levels. Ageing Soc 2020;40(12):
2593e613.
[15] Management MOH. Of type 2 diabetes mellitus. fifth ed.
Kuala Lumpur, Malaysia: Ministry of Health Malaysia; 2015.
http://www.acadmed.org.my.
[16] van Uffelen JG, Khan A, Burton NW. Gender differences in physical activity motivators and context preferences: a pop- ulation-based study in people in their sixties. BMC Publ Health 2017;17(1):624. PMID: 28676081.
[17] Paley CA, Johnson MI. Abdominal obesity and metabolic syndrome: exercise as medicine? BMC Sports Sci Med Rehabil 2018;10(1):7. PMID: 29755739.
[18] Rosano C, Longstreth Jr WT, Boudreau R, Taylor CA, Du Y, Kuller LH, et al. High blood pressure accelerates gait slowing in well-functioning older adults over 18-years of follow-up. J Am Geriatr Soc 2011;59(3):390e7. PMID:
21391929.
[19] Buford TW. Hypertension and aging. Ageing Res Rev 2016;
26:96e111. PMID: 25368028.
[20] Kalyani RR, Metter EJ, Egan J, Golden SH, Ferrucci L.
Hyperglycemia predicts persistently lower muscle strength with aging. Diabetes Care 2015;38(1):82e90. PMID:
25392294.
[21] Yoon JW, Ha YC, Kim KM, Moon JH, Choi SH, Lim S, et al.
Hyperglycemia is associated with impaired muscle quality in older men with diabetes: the Korean longitudinal study on health and aging. Diabetes Metab 2016;40(2):140e6. PMID:
27126884.
ALARTICLE
