Journal of Clinical Images and Medical Case Reports

ISSN 2766-7820
Research Article - Open Access, Volume 4

Accuracy of subjective and objective physical activity measurement methods for prediction of possible sarcopenia

Cheng Zhu1; Yanan Zhao1*; Ying Du1,2

1School of Sports Science and Physical Education, Nanjing Normal University, Nanjing, Jiangsu, China.

2Tsinghua Middle School, Xuzhou, Jiangsu, China.

*Corresponding Author : Yanan Zhao
School of Sports Science and Physical Education, Nanjing Normal University, Nanjing, Jiangsu, China.
Email: [email protected]

Received : Jan 07, 2023

Accepted : Jan 26, 2023

Published : Feb 02, 2023

Archived : www.jcimcr.org

Copyright : © Zhao Y (2023).

Abstract

Background: Early detection and prevention of sarcopenia arecritical. There is a close association between sarcopenia and physical activity levels. Possible sarcopenia is a precursor to sarcopenia, which can accurately predict sarcopenia. According to the tertiary prevention system, the diagnosis of possible sarcopenia has significant implications for the early detection of sarcopenia and the reduction of its prevalence.

Objective: This study aimed to investigate the relationship between subjective and objective physical activity measurement methods with possible sarcopenia and the prediction of it.

Methods: A total of 146 community-dwelling older adults (≥65 years old) in Nanjing were recruited to measure fundamental physical indicators and possible sarcopenia indicators. According to the Asian Sarcopenia Working Group (AWGS) diagnostic criteria for possible sarcopenia, the participants were divided into possible and non-possible sarcopenia groups. The participant’s physical activity level was measured using PASE and Acti Graph (GT3X).

Results: PASE showed a significant correlation between grip strength and the 5-time stand-to-sit test. GT3X had many indicators that showed a significant correlation with possible sarcopenia indicators. The Area Under The Curve (AUC) of PASE was 0.402 (p=0.167). The AUC of GT3X was 0.856 (p< 0.05). The AUC of PASE combined with GT3X was 0.855 (p< 0.05).

Conclusion: Objective measurement methods have more indicators correlated with diagnostic indicators of possible sarcopenia than subjective measurement methods and have higher correlation and prediction accuracy. The prediction accuracy of subjective combined with objective measurement methods is higher than using subjective measurement methods alone, and combined application may improve the accuracy of identifying possible sarcopenia in primary care settings.

Keywords: Physical activity; Measurement; Possible sarcopenia; Prediction.

Citation: Zhu C, Zhao Y, Du Y. Accuracy of subjective and objective physical activity measurement methods for prediction of possible sarcopenia. J Clin Images Med Case Rep. 2023; 4(2): 2269.

Introduction

In recent years, the population of aging tendency in China has been increasingly severe; in 2050, the proportion of older adults will account for more than 25% of the total population [1]. With the advancing age, many organs and functions of human body the inevitably appear to deteriorate and decline, causing numerous adverse effects [2-4]. Aging may lead to a decrease in myocyte regenerative capacity, an imbalance in protein turnover, and a change of fat and fibrotic components in the muscle [5], leading to the incidence of sarcopenia. Sarcopenia is a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, which is closely associated with adverse outcomes such as disability, decreased quality of life, and death [6]. Older adults have a high prevalence of sarcopenia [7]. The current prevalence of sarcopenia in Chinese communities is about 10.4% [8], and the numwhichwith age. Early identification and timely intervention are of great importance [9].

In 2019, the Asian Working Group On Sarcopenia (AWGS) was updated based on the previous consensus [10,11], and introduced the definition of possible sarcopenia and its diagnostic criteria. Possible sarcopenia is the decline of muscle strength accompanying with or without the decline of physical performance. Diagnostic indicators includ muscle mass (Calf Circumference (CC) by SARC-F scale) and muscle function (grip strength by the 5 Chair Sit-To-Stand Test; 5TSTS) [12,13]. Possible sarcopenia showed high prediction valuefor sarcopenia [14]. According to the tertiary prevention system, the diagnosis of possible sarcopenia has great significance for early identification and prevention for sarcopenia [15].

There are Various predicting and detecting sarcopenia including anthropometric prediction equations [16], and biomarker methods [17]. From the perspective of physical activity, older adults with sedentary lifestyles are at high risk of sarcopenia [18], indicating a close relationship between physical activity level and sarcopenia. However, there are few studies on the associations between possible sarcopenia and physical activity level. To date, both subjective and objective methods exsit for testing physical activity levels. Given the economical and convenient property, the subjective method would be preferred for population survey [19,20] but may be lack of accuracy. Objective measurements have higher accuracy but would be costly and difficult for translation [21,22].

The Physical Activity Scale for the Elderly (PASE) is a reliable and valid subjective measurement method of physical activity for older adults. It was designed to measure work-related, household, and leisure time activities in the past week [23]. The triaxial accelerometer Acti Graph (GT3X) is a wearable objective measurement that can measure the people’s physical activity burden and its accuracy has been proved [24-26]. This study aimed to investigate the relationship and predictive role of PASE and GT3X as representatives of subjective and objective measurements of physical activity with possible sarcopenia.

Methods

Participants

This study enrolled 146 community-dwelling older adults (≥65 yrs) from Nanjing (Men=41). The qualified participants should be those with independent living ability, without mobility, communication and cognitive impairment. Excluded criteria were physical disabilities, recently received major surgery or surgery history less than 6 months, with severe chronic diseases. All participants have signed the consent form before tests. This study has got the approval from the Nanjing Normal University Ethical Review Committee (NNU202206009).

Procedures

In the first place, participants finished the basic information scale and PASE. Subsequently, participants, underwent basic body indicators (height, weight, BMI, waist circumference, hip circumference) and possible sarcopenia indicators (CC, left/right hand grip strength, 5TSTS) evaluation. Lastly, we helped participants wear the GT3X correctly at the right hip after the test. We ensured safety during the test. The period of GT3Xs’ measurement was 4 days after the field test (r2 = 0.91). Each participant was required to wear it all day during the period of measurement, except for bathing and swimming, when it could be temporarily removed. Recycle after 4 days, followed by data processing and analysis.

Measures

Participants basic body indicators

The basic body indicators included height (m), weight (kg), BMI (kg/m2), waist circumference (cm), and hip circumference (cm). Using the body counter to measure the height and weight. BMI was calculated using the following equation: BMI = actual weight (kg) / squared height (m)2. Waist circumference, hip circumference, and CC were measured using a measuring tape. Waist circumference was the distance between the midpoint of the lower last rib and the midpoint of the superior iliac crest of participants [27]. Hip circumference was measured around the most prominent part of the hip horizontally.

Possible sarcopenia indicators

Possible sarcopenia indicators include CC (cm), left/right-hand grip strength (kg), and 5TSTS (s). The CC was measured with the patient in a sitting position, knee at 90◦ flexion, and the maximum circumference measured on each side [28] (cutoff values were < 34 cm for men and < 33 cm for women as proposed by the AWGS 2019). Grip strength was measured using a grip strength gauge (Model JAMAR 30107041, Preston, Jackson, MI.), with the patient in the sitting position at 90◦ elbow flexion; the maximum value from measuring each side three times was recorded (cutoff values of < 28 kg for men, < 18 kg for women). The 5TSTS was measured as the time taken to rise from a seated position and sit down five times as quickly as possible. Patients were instructed to cross their arms in front of their chest and not use the armrests as support during the 5TSTS [29]. The test was repeated three times and taken the best performance (cutoff value of ≥ 12 s).

Physical activity assessment

The Chinese version of PASE was a subjective physical activity measurement method, used to assess participants’usual physical activity. The PASE has 12 components pertaining to leisure time activity (five components), household activity (six components) and work-related activity (one component) over the past 7 days. In this study, the Chinese Jia-Yi Wu modified version of PASE by Jia-Yi Wifications included exercise types taking into account the frequently participated exercise types of Chinese older adults. The modified PASE showed high reliability and validity. PASE scores ranged from 0 to 360 or more where higher scores represent higher physical activity levels [30]. The total PASE score was computed by multiplying the amount of time spent in each activity (hours/week) or participation (yes/no) in an activity by the empirically derived item weights and summing over all activities.

The GT3X was an objective physical activity measurement method, used to masure the intensiolder adults’of physical activity of older atudy, the data were analyzed based on Actilife (Version 6.13.3) to obtain indicators of Physical Activity Energy Expenditure (PAEE), step counts, MET, counts, and other indicators, where the PAEE formula was chosen from Freedson Comination (1998), step counts and MET calculation formula was chosen from Freedon Adult (1998). Sedentary (SED) Time, Light Physical Activity (LPA) Time, and Moderate and Vigorous Physical Activity (MVPA) Time were obtained based on physical activity intensity thresholds [31] (Table 1).

Table 1: Physical activity intensity thresholds.
Variables Ranges
SED < 100 cpm
LPA 100 cpm-1951 cpm
MVPA 1952 cpm-9498 cpm

Statistical analysis

The participants were assigned to the “possible sarcopenia” and “non-possible sarcopenia” groups using excel according to the AWGS 2019 criteria. The baseline characteristics of the sample are expressed as mean ± standard deviation. The differences between the two groups were analyzed using the independent t-test. Using Spearman’s rank correlation coefficient to explore the correlation of possible sarcopenia indicators with PASE scores and GT3X indicators. Forward, associated indicators were analyzed by binary logistic regression. A Receiver Operating Characteristic (ROC) analysis assessed the area under the curve and determined the PASE, GT3X and the combination of both for correctly discriminate possible sarcopenia status along with sensitivity and specificity. All analyses were computed using Statistical Package for Social Sciences (SPSS, version 26, Chicago, IL).

Results

Baseline characteristics of all participants

The sample consisted of 146 older adult participants, with a mean age of 72.6 ± 5.6 years, 105 (72%) were female and 41 (28%) were male. Table 2 showed the baseline characteristics of all participants, including body indicators, possible sarcopenia indicators, PASE score, and GT3X indicators, according to AWGS definition and diagnostic algorithm.

In body indicators, weight and BMI showed significant differences between the two groups (p< 0.05). The possible sarcopenia group had lower body weight overall. Possible sarcopenia indicators showed significant differences between the two groups (p< 0.05), indicating that the possible sarcopenia group may have impairment in muscle function. There was no significant difference in PASE scores between the two groups. However, in GT3X indicators, PAEE and MET showed significant differences, indicating that physical activity level was lower in possible sarcopenia patient. These results were consistent with previous studies [32].

The correlation between PASE, GT3X and possible sarcopenia indicators

Results of the correlation between PASE and possible sarcopenia indicators showed in Table 3. Leisure time activity score was significantly correlated with left/right hand grip strength (ρs=0.267/ρs=0.235,p=0.05), and 5TSTS (ρs=﹣0.164, p< 0.05). Household activity score was significantly negatively correlated with 5TSTS (ρs=﹣0.213, p< 0.05). Total PASE score was significantly correlated with left/right hand grip strength (ρs=0.205/ρs=0.183, p < 0.05) and 5TSTS (ρs=﹣0.297, p< 0.05), which could indicate muscle strength.

Results of the correlation between GT3X and possible sarcopenia indicators showed in Table 4. Compared with PASE, GT3X had more significantly correlated indicators with possible sarcopenia indicators. The SED was significantly negatively correlated with right grip strength (ρs=-0.178, p< 0.05). The LPA was significantly negatively correlated with 5TSTS (ρs=-0.183, p< 0.05). The MVPA was significantly positively correlated with left/right hand grip strength (ρs=0.299/ρs=0.338, p< 0.05) and negatively correlated with the 5TSTS (ρs=-0.372, p< 0.05). Step counts and counts were significantly correlated with left/right hand grip strength and the 5TSTS. PAEE and MET were significantly correlated with all possible sarcopenia indicators in different degrees. Previous studies also showed that older adults with sarcopenia had lower energy expenditure in physical activity [33]. The results of this study added to the supporting evidence.

Prediction accuracy of PASE and GT3X for possible sarcopenia

The Area Under the Curve (AUC) using total PASE score was 0.402 (95% CI=0.256-0.548; p=0.167) with a sensitivity of 26.3% and a specificity of 81.1%. The AUC using GT3X was 0.856 (95% CI = 0.774-0.939; p< 0.05), with a sensitivity of 84.2% and a specificity of 79.5%. The AUC using PAEE, MET, MVPA, and counts were 0.263, 0.268, 0.355 and 0.337 (p< 0.05). The AUC using PASE combined with GT3X was 0.855 (95% CI=0.772-0.939; p 0.05), with a sensitivity of 84.2% and a specificity of 80.3%. The results showed GT3X had higher prediction accuracy than PASE for possible sarcopenia, and PASE combined with GT3X had higher prediction accuracy than PASE alone.

Table 2: Baseline characteristics of all participant.
Characteristics Total (n=146, 100%) Possible sarcopenia
Yes (n=19, 13%) No (n=127, 87%) p-value
Body indicators
Age, years 72.6 ± 5.6 75.0 ± 6.3 72.3 ± 5.5 0.054
Female, n (%) 105 (72%) 12 (63%) 93 (73%)
Height, cm 157.8 ± 7.7 156.2 ± 6.6 158.0 ± 7.8 0.337
Weight, kg 61.9 ± 9.6 54.5 ± 7.1 63.0 ± 9.4 < 0.001*
BMI, kg/m2 24.8 ± 3.3 22.5 ± 3.2 25.2 ± 3.2 0.001*
Possible sarcopenia indicators
CC(L), cm 34.2 ± 2.8 31.2 ± 1.1 34.7 ± 2.7 < 0.001*
CC(R), cm 34.3 ± 2.8 31.2 ± 1.3 34.7 ± 2.7 < 0.001*
Grip strength (L), kg 22.9 ± 8.0 16.8 ± 5.4 23.8 ± 7.9 23.8 ± 7.9
Grip strength (R), kg 23.5 ± 7.8 16.7 ± 4.5 24.5 ± 7.7 < 0.001*
5TSTS, s 11.6 ± 3.6 13.8 ± 4.7 11.3 ± 3.3 0.004*
PASE component
Leisure time activity 34.0 ± 27.1 26.1 ± 18.5 35.1 ± 28.1 0.177
Household activity 77.1 ± 36.0 64.7 ± 40.8 78.9 ± 35.0 0.109
Work-related activity 5.7 ± 25.5 6.6 ± 24.3 5.5 ± 25.7 0.868
Work-related activity 5.7 ± 25.5 6.6 ± 24.3 5.5 ± 25.7 0.868
PASE score 112.6 ± 53.8 97.4 ± 56.5 114.9 ± 53.3 0.187
GT3X indicators
SED, min/wk 4628.1 ± 734.8 4656.7 ± 755.7 4623.8 ± 734.6 0.856
LPA, min/wk 1195.5 ± 566.6 987.8 ± 434.2 1226.6 ± 578.8 0.087
MVPA, min/wk 1061.8 ± 869.3 774.9 ± 1036.4 1104.8 ± 837.7 0.123
PAEE, kcal 1908.5 ± 1218.7 1101.7 ± 657.7 2029.2 ± 1238.7 < 0.001*
step counts, steps/wk 53855.7 ± 30206.0 49003.3 ± 33332.8 54581.7 ± 29785.5 0.455
MET 7.9 ± 0.8 7.4 ± 0.4 8.0 ± 0.8 < 0.001*

BMI: Body Mass Index; SED: Sitting Activity Time; LPA: Light Physical Activity; MVPA: Moderate And Vigorous Physical Activity; PAEE: Physical Activity Energy Expenditure; CC: Calf Circumference; 5TSTS: 5-Time Stand-To-Sit Test.

Table 3: PASE and possible sarcopenia indicators.
Leisure time activity Household activity Work-related activity PASE score
ρs p-value ρs p-value ρs p-value ρs p-value
CC(L), cm 0.097 0.244 0.034 0.682 0.115 0.168 0.060 0.469
CC(R), cm 0.097 0.245 0.057 0.496 0.089 0.287 0.084 0.312
Grip strength (L), kg 0.267* 0.001 0.135 0.103 0.153 0.066 0.205* 0.013
Grip strength (R), kg 0.235* 0.004 0.132 0.112 0.157 0.059 0.183* 0.027
5TSTS, s ﹣0.164* 0.049 ﹣0.213* 0.010 ﹣0.056 0.499 ﹣0.297* < 0.01

CC: Calf Circumference; 5TSTS: 5-Time Stand-To-Sit Test.

Table 4: GT3X and possible sarcopenia indicators.
SED LPA MVPA PAEE Step counts MET Counts
ρs ρs ρs ρs ρs ρs ρs
CC(L), cm 0.032 0.052 0.078 0.334* 0.006 0.262* 0.039
CC(R), cm 0.052 0.049 0.049 0.310* -0.033 0.231* 0.032
Grip strength (L), kg -0.147 0.069 0.299* 0.327* 0.253* 0.370* 0.190*
Grip strength (R), kg -0.178* 0.079 0.338* 0.367* 0.276* 0.409* 0.237*
5TSTS, s 0.038 -0.183* -0.372* -0.366* -0.359* -0.371* -0.332*

*p< 0.05; s: Spearman’ s rank correlation coefficient
CC: Calf Circumference; 5TSTS: 5-Time Stand-To-Sit Test; Sed: Sitting Activity Time; LPA: Light Physical Activity; MVPA: Moderate And Vigorous Physical Activity; PAEE: Physical Activity Energy Expenditure.

Table 5: ROC results.
AUC (95% CI) Sensitivity (95% CI) Specificity (95% CI) p-value
PASE 0.402 26.3% 81.1% 0.167
GT3X 0.856 84.2% 79.5% < 0.001
PASE and GT3X 0.855 84.2% 80.3% < 0.001

Discussion

This study investigated the relationship and predictive role of PASE and GT3X as representatives of subjective and objective measurement methods of physical activity with possible sarcopenia. In previous studies, insufficient physical activity is an important risk factor for sarcopenia in older adults [34]. In this study, two physical activity measurement methods selected showed correlated with possible sarcopenia diagnostic indicators in different degrees, further providing indirect evidence for the association between sarcopenia and physical activity.

In this study, possible sarcopenia group had significantly lower weight and BMI than normal values. The aging accompanied by weight loss usually indicates that some underlying disease is occurring. It not only indicates the possibility of sarcopenia but is also one of the characteristics of the state of “nutritional weakness”. “Nutritional weakness” is the sign of organic weakness eventually. The underweight older adults can take steps consciously to improve their current physical status [35]. In addition, the overweight rate among Chinese adults has increased significantly in recent years [36]. In this study, the BMI of all participants was higher than the standard, in the overweight range [37]. It might be due to the overall overweight phenomenon caused by the BMI in the non-possible sarcopenia group being generally higher than normal. Further calculated the Waist-To-Hip Ratio (WHR) of all participants. WHR can better predict several metabolic risk factors [38]. According to the threshold value of WHR for obesity [39], 82 women and 23 men were higher than the standard, accounting for 72% of the total sample. The data reflects the severity of the overweight phenomenon.

The prevalence of possible sarcopenia was 17% and 11.4% respectively in the male and female elderly in this study. There are different findings about the association between gender and sarcopenia. Some studies showed that the decrease in muscle mass is more severe in older men than women [40,41]. The prevalence of sarcopenia was 50% in 80-year-old men, compared to 43.8% in women in the same age group [42-44]. Another study showed a higher prevalence of sarcopenia in women under 80 years of age. The faster decrease in steroid hormones in women compared to men might be a potential reason [45]. A variety of endogenous and exogenous factors may influence the prevalence of sarcopenia [46]. In future studies, a larger sample size is needed to further explore the association between gender and possible sarcopenia.

In this study, the PASE scores of the possible sarcopenia and non-possible sarcopenia groups did not show significant differences. The reason might be the small sample size of this study. PASE was significantly correlated with muscle strength indicators. It is further speculated that PASE could identify a decrease in muscle strength for the purpose of predicting possible sarcopenia.

PAEE and MET were significantly lower in the possible sarcopenia group than non-possible sarcopenia group, indicating that the physical activity level of older adults with possible sarcopenia was lower than that of older adults without the disease. In addition, many indicators of GT3X showed different degrees of significant correlation with indicators of possible sarcopenia, indicating an association between GT3X and possible sarcopenia. This result was consistent with previous studies using subjective and objective measurement methods to measure physical activity. A longitudinal survey of physical activity level and lean body mass in Japanese residents aged 65-84 showed that older adults with high physical activity level scores on objective measures were at lower risk of having muscle mass below the sarcopenia threshold than sedentary older adults [47]. In a study of the relationship between physical activity and sarcopenia in older Koreans, the results showed that increased physical activity was associated with a reduced risk of sarcopenia in older adults [48]. Further demonstrated the importance of the physical activity to maintain muscle function and prevent sarcopenia.

The results showed that GT3X had more indicators significantly correlated with possible sarcopenia diagnostic indicators than PASE, and the degree of correlation and AUC were also higher. Suggesting that objective measurement methods might be better predictors of possible sarcopenia than subjective measurement methods. Such results were consistent with the previous study [49]. There were also many studies with different opinions. In a study to assess the prevalence of endometrial cancer in women, subjective measurement methods showed the same or even better results than objective measurements methods [50]. In a previous study assessing sleep quality, it was shown that both subjective and objective measurement methods should be included in the study for a comprehensive assessment [51]. The results of this study support such an argument. The prediction accuracy of PASE in combination with GT3X is higher than that of PASE alone. In summary, this study showed that objective measurement methods had better prediction accuracy than subjective measurement methods, and the combination of both methods had higher prediction accuracy than using subjective measurement methods alone. Considering the different characteristics of subjective and objective measurement methods, the combination may be more effective in predicting other physical activity-related disorders in the future. Further research is needed in the future to investigate the degree of reproducibility of subjective or objective measurement methods.

On the one hand, in this study, the participants were community-dwelling older adults who participated in the study voluntarily. We were unable to accurately plan the number of male and female participants, so there were differences in the number of subjects by gender. On the other hand, GT3X was worn at the hip, which might be difficult to measure some upper body movements accurately, such as climbing and weight-bearing [52].

Conclusion

Objective measurement methods showed more indicators correlated with diagnostic indicators of possible sarcopenia than subjective measurement methods, and the degree of correlation and prediction accuracy were also higher. GT3X as an objective measurement method showed better prediction accuracy for possible sarcopenia. The prediction accuracy of subjective measurement methods combined with objective was better than using subjective measurement methods alone. The combination of both might improve the accuracy of identifying possible sarcopenia in the primary care setting.

Declarations

Funding: This research received no external funding.

Author contributions: CZhu: data analysis, writing-original draft. YD: data collection. YNZ: conceptualization, methodology, review & editing, supervision.

Institutional review board statement: All procedures were approved by the Nanjing Normal University Ethical Review Committee (NNU202206009).

Informed consent statement: Informed consent was obtained from all participants involved in the study.

Data availability statement: The data presented in this study are available on request from the first author.

Conflicts of interest: The authors declare no conflict of interest.

References

  1. Luo F, Zhou J. Advance in rehabilitation for sarcopenia in old adults (review). Chin J Rehabil Theory Pract. 2018; 24: 256-259. (in Chinese)
  2. Bryant C, Bei B, Gilson K, Komiti A, Jackson H, et al. The relationship between attitudes to aging and physical and mental health in older adults. International psychogeriatrics. 2012; 24: 1674-1683.
  3. Zhang WD, Guo YW, Lu Z W. Aging ‘s Aggregateand Structural Impact on Economy. Financial Development. 2021; 02: 78-88. (in Chinese)
  4. Zhou BY, Yu PL. Pay attention to the prevention and treatment of sarcopenia in the elderly. Chinese Journal of Practical Internal Medicine. 2022; 42: 617-619. (in Chinese)
  5. Kim JW, Kim R, Choi H, Lee SJ, Bae GU. Understanding of sarcopenia: From definition to therapeutic strategies. Arch Pharm Res. 2021; 44: 876-889.
  6. Cruz-Jentoft AJ. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010; 39: 412-423.
  7. Cosquéric G, Sebag A, Ducolombier C, Thomas C, Piette F, Weill-Engerer S. Sarcopenia is predictive of nosocomial infection in care of the elderly. Br J Nutr. 2006; 96: 895-901.
  8. Wang H, Hai S, Cao L, Zhou J, Liu P, Dong BR, et al. Estimation of prevalence of sarcopenia by using a new bioelectrical impedance analysis in Chinese community-dwelling elderly people. BMC Geriatr. 2016; 16: 216.
  9. Gao C, Yu PL. The importance of the prevention and management of sarcopenia in the elderly. Chinese Journal of Clinical Healthcare. 2021; 24: 433-436. (in Chinese)
  10. Chen LK., WooJ Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020; 21: 300-307.
  11. Chen, L. K., Liu, L. K., Woo, J., Assantachai, P., Auyeung, T. W., Bahyah, K. S, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014; 15: 95-101.
  12. Asai C, Akao K, Adachi T, Iwatsu K, Fukuyama A, Ikeda M, et al. Maximal calf circumference reflects calf muscle mass measured using magnetic resonance imaging. Arch Gerontol Geriatr. 2019; 83: 175-178.
  13. Bahat G, Yilmaz O, Kılıç C, Oren MM, Karan MA, et al. Performance of SARC-F in Regard to Sarcopenia Definitions, Muscle Mass and Functional Measures. J Nutr Health Aging. 2018; 22: 898-903.
  14. Ueshima J, Maeda K, Shimizu A, Inoue T, Murotani K, et al. Diagnostic accuracy of sarcopenia by “possible sarcopenia” premiered by the Asian Working Group for Sarcopenia 2019 definition. Arch Gerontol Geriatr. 2021; 97: 104484.
  15. Joseph CL, Williams LK, Ownby DR, Saltzgaber J, Johnson CC. Applying epidemiologic concepts of primary, secondary, and tertiary prevention to the elimination of racial disparities in asthma. J Allergy Clin Immunol. 2006; 117: 233-242.
  16. Beaudart C, Mc Closkey E, Bruyère O, Cesari M, Rolland Y. et al. Sarcopenia in daily practice: assessment and management. BMC Geriatr. 2016; 16: 170.
  17. Kwak JY, Hwang H, Kim SK, Choi JY, Lee SM, et al. Prediction predictionnia using a combination of multiple serum biomarkers. Sci Rep. 2018; 8: 8574.
  18. Rogers ME, Rogers NL, Takeshima N, Islam MM. Methods to assess and improve the physical parameters associated with fall risk in older adults. Prev Med. 2003; 36: 255-264.
  19. Van Boxtel MP, Langerak K, Houx PJ, Jolles J. Self-reported physical activity, subjective health, and cognitive performance in older adults. Experimental aging research. 1996; 22: 363-379.
  20. Skatrud-Mickelson M, Benson J, Hannon JC, Askew EW. A comparison of subjective and objective measures of physical exertion. J Sports Sci. 2011; 29: 1635-1644.
  21. Buchowski MS, Townsend KM, Chen KY, Acra SA, Sun M, et al. Energy expenditure determined by self-reported physical activity is related to body fatness. Obes Res. 1999; 7: 23-33.
  22. Hjelm EW, Winkel J, Nygård CH, Wiktorin C, Karlqvist L, et al. Can cardiovascular load in ergonomic epidemiology be estimated by self-report? Stockholm MUSIC 1 Study Group. J Occup Environ Med. 1995; 37: 1210-1217.
  23. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA, et al. The Physical Activity Scale for the Elderly (PASE): Evidence for validity. J Clin Epidemiol. 1999; 52: 643-651.
  24. Gorber SC, Tremblay MS. Self-report and direct measures of health: bias and implications. The objective monitoring of physical activity: Contributions of accelerometry to epidemiology, exercise science and rehabilitation. Springer, Cham. 2016: 369-376.
  25. Kelly LA, McMillan DG, Anderson A, Fippinger M, Fillerup G, Rider J, et al. Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med Phys. 2013; 13: 5.
  26. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011; 14: 411-416.
  27. Ness-Abramof R, Apovian CM. Waist circumference measurement in clinical practice. Nutr Clin Pract. 2008; 23: 397-404.
  28. Nishioka S, Yamanouchi A, Matsushita T, Nishioka E, Mori N, Taguchi S, et al. Validity of calf circumference for estimating skeletal muscle mass for Asian patients after stroke. Nutrition. 2021; 82: 111028.
  29. Zhanga A Li, YX, Li XL, YinY, Li, RL, QiaoX, et al. A comparative study of the five-repetition sit-to-stand test and the 30-second sit-to-stand test to assess exercise tolerance in COPD patients. Int J Chron Obstruct Pulmon Dis. 2018; 13: 2833-2839.
  30. Gao XG. Research on the Relationship Between Physical Activity of the Elderly and Medical Utilization as well as Quality of Life. Shanghai University of Sport, Shanghai. 2012; https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2016&filename=1015626452.nh. (in Chinese)
  31. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998; 30: 777-781.
  32. Verlaan S, Aspray TJ, Bauer JM, Cederholm T, Hemsworth J, Hill TR, et al. Nutritional status, body composition, and quality of life in community-dwelling sarcopenic and non-sarcopenic older adults: A case-control study. Clin Nutr. 2017; 36: 267-274.
  33. Bunout D, Barrera G, Hirsch S, Jimenez T, de la Maza MP. Association between activity energy expenditure and peak oxygen consumption with sarcopenia. BMC Geriatr. 2018; 18: 298.
  34. Steffl M, Bohannon RW, Sontakova L, Tufano JJ, Shiells K, Holmerova I. Relationship between sarcopenia and physical activity in older people: a systematic review and meta-analysis. Clin Interv Aging. 2017; 12: 835-845.
  35. Bales CW, Ritchie CS. Sarcopenia, weight loss, and nutritional frailty in the elderly. Annu Rev Nutr. 2002; 22: 309-323.
  36. Ma S, Xi B, Yang L, Sun J, Zhao M, Bovet P, et al. Trends in the prevalence of overweight, obesity, and abdominal obesity among Chinese adults between 1993 and 2015. Int J Obes (Lond). 2021; 45: 427-437.
  37. Chen CM. Overview of obesity in Mainland China. Obes Rev. 2008; 9: 14-21.
  38. KoK. P, Oh DK, Min H, Kim CS, Park JK, et al. Prospective study of optimal obesity index cutoffs for predicting development of multiple metabolic risk factors: the Korean genome and epidemiology study. J Epidemiol. 2012; 22: 433-439.
  39. World Health Organization. The Asia-Pacific perspective: Redefining obesity and its treatment. J. 2000.
  40. Gallagher D, Visser M, De Meersman RE, Sepúlveda D, Baumgartner RN, et al. Appendicular skeletal muscle mass: Effects of age, gender, and ethnicity. J Appl Physiol. 1997; 83: 229-239.
  41. Forbes GB. Longitudinal changes in adult fat-free mass: Influence of body weight. Am J Clin Nutr. 1999; 70: 1025-1031.
  42. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, et al. Epidemiology of sarcopenia among the elderly in New Mexico. American journal of epidemiology. 1998; 147: 755-763.
  43. Iannuzzi-Sucich M, Prestwood KM, Kenny AM. Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women. J Gerontol A Biol Sci Med Sci. 2002; 57: M772-M777.
  44. Kirchengast S, Huber J. Gender and age differences in lean soft tissue mass and sarcopenia among healthy elderly. Anthropol Anz. 2009; 67: 139-151.
  45. Juul A, Skakkebaek NE. Androgens and the ageing male. Hum Reprod Update. 2002; 8: 423-433.
  46. Shafiee G, Keshtkar A, Soltani A, Ahadi Z, Larijani B, Heshmat R, et al. Prevalence of sarcopenia in the world: A systematic review and meta- analysis of general population studies. J Diabetes Metab Disord. 2017; 16: 21.
  47. Shephard RJ, Park H, Park S, Aoyagi Y. Objectively measured physical activity and progressive loss of lean tissue in older Japanese adults: Longitudinal data from the Nakanojo study. J Am Geriatr Soc. 2013; 61: 1887-1893.
  48. Ryu M, Jo J, Lee Y, Chung YS, Kim KM, Baek WC, et al. Association of physical activity with sarcopenia and sarcopenic obesity in community-dwelling older adults: The Fourth Korea National Health and Nutrition Examination Survey. Age Ageing. 2013; 42: 734-740.
  49. Zhang GH, Fenton RS, Rival R, Solomon P, Cole P, Li Y, et al. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2008; 43: 484-489.
  50. Mascilini F, Testa AC, Van Holsbeke C, Ameye L, Timmerman D, Epstein E, et al. Evaluating myometrial and cervical invasion in women with endometrial cancer: Comparing subjective assessment with objective measurement techniques. Ultrasound Obstet Gynecol. 2013; 42: 353-358./li>
  51. Zhang L, Zhao ZX. Objective and subjective measures for sleep disorders. Neurosci Bull. 2007; 23: 236-240.
  52. Sun F, Norman IJ, While AE. Physical activity in older people: A systematic review. BMC Public Health. 2013; 13: 449.