Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Agreement between upper and lower limb measures to identify older adults with low skeletal muscle strength, muscle mass and muscle quality

  • Charles Phillipe de Lucena Alves,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Marcyo Câmara,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Geovani Araújo Dantas Macêdo,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Yuri Alberto Freire,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Raíssa de Melo Silva,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Ronildo Paulo-Pereira,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Luiz Fernando Farias-Junior,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Graduate Programa in Psychobiology, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Ana Paula Trussardi Fayh,

    Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliations Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil, Graduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil, Graduate Program in Nutrition, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Arnaldo Luis Mortatti,

    Roles Conceptualization, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯
  • Eduardo Caldas Costa

    Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    ecc@ufrnet.br

    Affiliations Graduate Program in Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil, Graduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil

    ⨯

Abstract

Background

Identifying low skeletal muscle strength (SMS), skeletal muscle mass (SMM) and skeletal muscle quality (SMQ) is pivotal for diagnosing sarcopenia cases. Age-related declines in SMS, SMM, and SMQ are dissimilar between the upper (UL) and lower limbs (LL). Despite this, both UL and LL measures have been used to assess SMS, SMM and SMQ in older adults. However, it is not clear whether there is agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ.

Objective

To investigate the agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ.

Methods

Participants (n = 385; 66.1 ± 5.1 years; 75,4% females) performed the handgrip strength test (HGS) and the 30-s chair stand test (CST) to assess UL- and LL-SMS, respectively. The SMM was assessed by dual-energy X-ray absorptiometry (DXA). The UL-SMQ was determined as: handgrip strength (kgf) ÷ arm SMM (kg). LL-SMQ was determined as: 30-s CST performance (repetitions) ÷ leg SMM (kg). Results below the 25th percentile stratified by sex and age group (60–69 and 70–80 years) were used to determine low SMS, SMM and SMQ. Cohen’s kappa coefficient (κ) was used for the agreement analyses.

Results

There was a slight and non-significant agreement between UL and LL measures to identify older adults with low SMS (κ = 0.046; 95% CI 0.093–0.185; p = 0.352). There was a moderate agreement to identify low SMM (κ = 0.473; 95% CI 0.371–0.574; p = 0.001) and a fair agreement to identify low SMQ (κ = 0.206; 95% CI 0.082 to 0.330; p = 0.005).

Conclusion

The agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ is limited, which might generate different clinical interpretations for diagnosing sarcopenia cases.

Citation: Phillipe de Lucena Alves C, Câmara M, Dantas Macêdo GA, Freire YA, de Melo Silva R, Paulo-Pereira R, et al. (2022) Agreement between upper and lower limb measures to identify older adults with low skeletal muscle strength, muscle mass and muscle quality. PLoS ONE 17(1): e0262732. https://doi.org/10.1371/journal.pone.0262732

Editor: Alessandra Coin, Clinca Geriatrica, ITALY

Received: June 9, 2021; Accepted: January 4, 2022; Published: January 21, 2022

Copyright: © 2022 Phillipe de Lucena Alves et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All files are available from the Open Science Framework at: https://osf.io/suavh/ (DOI 10.17605/OSF.IO/SUAVH).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Aging is commonly accompanied by declines in skeletal muscle strength (SMS), skeletal muscle mass (SMM) and skeletal muscle quality (SMQ) [1–4]. SMQ can be defined in terms of muscle composition or relative strength [5, 6]. SMQ (as relative strength) describes the muscle’s ability to function and is operationally defined in terms of SMS normalized to SMM [5, 6]. Assessing SMS, SMM, and SMQ in older adults has been recommended by clinical guidelines. For example, the European Working Group on Sarcopenia in Older People (EWGSOP2) recommends assessing SMS, SMM, and SMQ to identify those who are at high risk for or have established sarcopenia [7]. In addition, low SMS, SMM, and SMQ are associated with a higher risk for several adverse health-related outcomes in older adults, such as reduced mobility [8], physical disability [8], frailty [9], falls [10], impaired health-related quality of life [11, 12], all-cause and cardiovascular mortality [13–16].

Age-related declines in SMS, SMM, and SMQ are dissimilar between the upper (UL) and lower limbs (LL) [1–4]. Despite this, both UL and LL measures have been used to assess SMS, SMM and SMQ in older adults [17–20]. The handgrip strength test (HGS) and the chair stand test (CST) have been commonly used in clinical practice to assess SMS in older adults. However, a previous study [20] demonstrated that the prevalence of older adults at high risk for (low SMS) and having established sarcopenia (low SMS + low SMM) was lower using HGS than the 5-repetition CST. In addition, the authors observed poor agreement between the HGS and the 5-repetition CST to identify both individuals at high risk for and having established sarcopenia, suggesting that the interchangeable use of these tests might generate different clinical interpretations for the EWGSOP2 algorithm [7]. Thus, more information is needed about the agreement between UL and LL measures to identify older adults with impaired neuromuscular characteristics. In view of this, the aim of this study was to investigate the agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ.

2 Methods

2.1 Study design

This was a cross-sectional study which is reported in accordance with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement [21]. This study was conducted at the Onofre Lopes University Hospital and at the Department of Physical Education of the Federal University of Rio Grande do Norte between June 2018 and December 2019. The Ethics Committee of the Federal University of Rio Grande do Norte approved this study (protocol number: 2.603.422/2018), which was conducted according to the Declaration of Helsinki. All participants were informed about the study procedures and gave written informed consent.

2.2 Participants

Community-dwelling older adults aged 60–80 years from the city of Natal, RN, Brazil were recruited to participate in this study by advertisements on radio, TV, e-flyers in social media sites, healthcare units, and older adult community centers. The inclusion criteria were: i) no history of known cardiovascular diseases or major cardiovascular events (e.g., acute myocardial infarction, stroke, coronary artery disease, arrhythmias, or peripheral vascular disease); ii) no muscle, joint or bone injury which limits the ability to perform exercise; iii) no acute diabetes- or hypertension-related decompensation (i.e. glycaemia ≥ 300 mg/dL; blood pressure ≥ 160/105 mmHg). Participants with incomplete data related to the strength tests or body composition assessment were excluded from the final analysis.

2.3 Skeletal muscle strength

2.3.1 Handgrip strength test.

The HGS was performed following the recommendations of Coldham [22] as a proxy of UL-SMS. All participants were seated in a straight-backed chair with their feet flat on the floor and positioned in a standardized position with their shoulder adducted and neutrally rotated, elbow flexed at 90°, forearm in a neutral rotation, and their wrist between 0° and 30° extension and between 0° and 15° ulnar deviation. All participants were instructed to squeeze the handgrip (Jamar® 5030J1) as hard as possible during a 5-second period with their dominant hand during the expiration phase, avoiding Valsalva’s maneuver. They performed three attempts with verbal encouragement interspersed by 1-minute interval between each attempt. The highest value observed in the three attempts was considered for data analysis.

2.3.2 30-s chair stand test.

The 30-s CST was performed following the recommendations of Rikli and Jones [23] as a proxy of LL-SMS. The participants were instructed to sit in the middle of the chair with their back straight, feet flat on the floor, and arms crossed at the wrists and held against their chest. On the signal “go”, they were instructed to rise to a full stand and then return to a fully seated position. All participants were verbally encouraged to complete as many full stands as possible within a 30-s period. The number of repetitions was considered for data analysis.

2.4 Skeletal muscle mass

Dual-energy X-ray absorptiometry (DXA) is a widely used technique which assesses body composition at the molecular level [24, 25]. It assesses the lean soft tissue (LST) or lean body mass, which is the sum of body water, total body protein, carbohydrates, non-fat lipids, and soft tissue mineral [24, 25]. Body composition was assessed by DXA (GE Healthcare® Lunar Prodigy Advance) following the recommendations of the National Health and Nutrition Examination Survey [26]. Participants’ weight (kg) and height (cm) were previously measured (Welmy® W300). Total-, arm- and leg-LST in kilograms were calculated by specific software (Encore, version 14.1) from the DXA scan. In this study, LST determined by the DXA technique was used as a proxy of total-, arm-, and leg-SMM [24], as recommended by the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis working group on frailty and sarcopenia.

2.5 Muscle quality

SMQ was determined in terms of UL- and LL-SMS (HGS and 30-s CST, respectively) normalized to appendicular skeletal muscle mass (ASM; kg) as assessed by DXA [5, 6]. Therefore, UL-SMQ was determined as: HGS (kgf) ÷ arm SMM (kg). LL-SMQ was determined as: 30-s CST performance (repetitions) ÷ leg SMM (kg).

2.6 Criteria for defining older adults with low SMS, SMM and SMQ

The UL and LL measures for SMS, SMM and SMQ were stratified into quartiles based on sex and age group (60–69 and 70–80 years). Males and females from each sex and age group who had UL and LL measures for SMS, SMM and SMQ below the 25th percentile were identified as older adults with low SMS, SMM and SMQ [23, 27].

2.7 Physical activity

Physical activity level was determined by the Brazilian version of the Minnesota Leisure Time Activities Questionnaire for older adults [28]. The physical activities were classified as light, moderate or vigorous considering the absolute intensity (metabolic equivalents; METs) for each specific age (40–64 years; ≥ 65 years), based on the American College of Sports Medicine [29]. Participants who performed ≥600 MET/min/wk of moderate-vigorous physical activities were considered as ‘active’, while those who performed < 600 MET/min/wk were considered as ‘inactive’.

2.8 Statistical analysis

Descriptive data are expressed as mean ± standard deviation, absolute and relative frequencies. Data normality was verified by Shapiro-Wilk and Q-Q plot tests. Cohen’s kappa coefficient (κ) was used to analyze the agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ. Cohen’s kappa coefficient (κ) < 0.00 was interpreted as poor agreement, 0.00–0.20 as slight agreement, 0.21–0.40 as fair agreement, 0.41–0.60 as moderate agreement, 0.61–0.80 as substantial agreement, and 0.81–1.00 as almost perfect [30]. The significance level was set at p < 0.05 for all analyses.

3 Results

A total of 385 older adults were included in the final analysis (Fig 1). Most participants were females (72.4%; n = 279), Caucasian (42%; n = 163) and ‘Pardos’ or Brown (49.7%; n = 193), lived with a partner (69.1%; n = 266), were overweight or obese (overweight: 40.0%, n = 154; obesity: 38.4%, n = 148), and had hypertension (52.5%; n = 202). Approximately one-third of the participants were ex-smokers (35.3%; n = 136) and had dyslipidemia (32.9%; n = 127). Few participants had post-secondary education (4.2%; n = 16), were smokers (3.4%; n = 13), or had diabetes (14.3%; n = 54). Additionally, 58.4% (n = 225) were physically active and 41.6% (n = 160) were physically inactive. Table 1 shows the neuromuscular characteristics of the participants.

thumbnail
Download:
Fig 1. Study flowchart.

https://doi.org/10.1371/journal.pone.0262732.g001

thumbnail
Download:
Table 1. Characteristics of the participants (n = 385).

https://doi.org/10.1371/journal.pone.0262732.t001

Table 2 shows the cut-offs (25th percentile) to identify older adults with low SMS, SMM and SMQ, according to sex and age group. Overall, the cut-offs for the neuromuscular characteristics were slightly lower for females and older adults aged 70–80 years.

thumbnail
Download:
Table 2. Cut-offs for upper and lower limb measures to identify older adults with low skeletal muscle strength, muscle mass, and muscle quality according to sex and age group.

https://doi.org/10.1371/journal.pone.0262732.t002

Table 3 shows the agreement analysis between UL and LL measures to identify older adults with low SMS, SMM, and SMQ. There was a slight and non-significant agreement between UL and LL measures to identify older adults with low SMS (κ = 0.046; 95% CI 0.093–0.185; p = 0.352). There was a moderate agreement between UL and LL measures to identify older adults with low SMM (κ = 0.473; 95% CI 0.371–0.574; p = 0.001). There was a fair agreement between UL and LL measures to identify older adults with low SMQ (κ = 0.206; 95% CI 0.082 to 0.330; p = 0.005).

thumbnail
Download:
Table 3. Agreement between upper and lower limb measures to identify older adults with low skeletal muscle strength, muscle mass and muscle quality.

https://doi.org/10.1371/journal.pone.0262732.t003

Tables 4 and 5 shows the agreement analysis between UL and LL measures to identify older males and females with low SMS, SMM, and SMQ. There was a slight and non-significant agreement between UL and LL measures to identify low SMS in older males (κ = 0.183; 95% CI -0.071–0.436; p = 0.059). There was a fair agreement between UL and LL measures to identify low SMM in older males (κ = 0.376; 95% CI -0.071–0.436; p = 0.001) and a fair agreement to identify low SMQ in older males (κ = 0.448; 95% CI 0.223 to 0.673; p = 0.001). There was a poor and non-significant agreement between UL and LL measures to identify low SMS in older females (κ = -0.001; 95% CI -0.166–0.164; p = 0.987). There was moderate agreement between UL and LL measures to identify low SMM in older females (κ = 0.507; 95% CI 0.384–0.629; p = 0.001) and a fair agreement to identify low SMQ in older females (κ = 0.126; 95% CI -0.019–0.271; p = 0.001).

thumbnail
Download:
Table 4. Agreement between upper and lower limb measures to identify older males with low skeletal muscle strength, muscle mass and muscle quality.

https://doi.org/10.1371/journal.pone.0262732.t004

thumbnail
Download:
Table 5. Agreement between upper and lower limb measures to identify older females with low skeletal muscle strength, muscle mass and muscle quality.

https://doi.org/10.1371/journal.pone.0262732.t005

4 Discussion

To the best of our knowledge, this is the first study which has investigated the agreement between UL and LL measures to identify older adults with low SMS, SMM and SMQ. The main findings indicate that: i) there was slight and non-significant agreement between UL and LL measures to identify older adults with low SMS; ii) there was a moderate and fair agreement between UL and LL measures to identify older adults with low SMM and SMQ, respectively.

Despite the HGS and 30-s CST being well-recognized tests to measure SMS, we did not observe a significant agreement between them to identify older adults with low SMS. The decline of SMS occurs in different magnitudes over aging in UL and LL [1–4, 31,32]. Frontera et al. [33] showed a decline of 1.4 and 2.5% per year in UL- and LL-SMS, respectively. Other studies have observed a higher magnitude of difference (i.e. a decline of 1.4 and 5.4% per year in UL- and LL-SMS, respectively) [19, 34]. It seems clear that the LL-SMS declines to a greater magnitude with aging than the UL-SMS, which can partially explain our findings. Recently, Yeung et al [35]. investigated the agreement between the HGS and knee extension strength performance in individuals from different age and health-status groups, in which they observed a low correlation between HGS and knee extension strength in healthy older adults and a moderate correlation in geriatric outpatients and older adults post-hip fracture. The authors found poor to moderate intraclass correlation coefficients between the tests. At an individual level, Bland-Altman plots indicated that the agreement between HGS and knee extension strength was lower among healthy older adults compared to geriatric outpatients and older adults post-hip fracture. Taken together, even using a different test to assess LL-SMS and other statistical approaches, the results from Yeung et al. [35] seem to be in accordance with our findings regarding the limited agreement between UL and LL measures to assess SMS in healthy older adults.

Another reason which can explain the non-significant agreement between the HGS and 30-s CST to identify older adults with low SMS is the characteristics of these tests. Although HGS and 30-s CST are valid proxies of SMS [36], the HGS measures the maximal isometric contraction, while the 30-s CST assesses the performance on a functional task, which seems to involve other fitness-related components in addition to SMS [23]. In accordance with our findings, Johansson et al. [20] found a poor agreement between HGS and 5-repetition CST to identify older adults at high risk for (κ = 0.07) and having established sarcopenia (κ = 0.18). Moreover, only 1.3% and 4.4% of the older adults were identified as having a high risk for or having established sarcopenia by both HGS and 5-repetition CST, respectively.

A significant agreement between UL and LL measures to identify low SMM in older adults was observed in the present study. Different from SMS, the decline of SMM seems to occur in a similar magnitude over aging in UL and LL in some studies [17, 37, 38], which may explain the agreement between UL and LL measures observed in this study. On the other hand, some studies show a reduction in different magnitudes between UL and LL, depending on how and where we measure [39, 40]. Despite this, the magnitude of this agreement was moderate. It is reasonable to think that other factors can explain the moderate agreement between UL and LL measures to identify low SMM in older adults. The DXA technique assesses the LST, which includes ~55% of SMM [24, 25]. The additional components of LST (body water, carbohydrates, nonfat lipids, and soft tissue mineral) can be different between UL and LL, which could also explain the moderate agreement observed between UL- and LL-SMM [41]. In addition, ~75% of SMM are concentrated in the LL and the rest is distributed in the trunk and in the UL [24]. This aspect can partially explain the moderate agreement between UL and LL measures to identify low SMM in older adults. Given that 60.4% of the older adults were identified as having low SMM by both arm and leg measures and 39.6% were identified as having low SMM in only one of these measures, it seems reasonable to assume that the UL and LL measures might induce different clinical interpretations regarding identification of low SMM in older adults.

Regarding the SMQ, which is an index derived from the SMS and SMM [6, 42], a significant but fair agreement was observed between UL and LL measures to identify low SMQ in older adults. Only 39.8% of the older adults were identified as having low SMQ by both UL and LL measures. We believe that this finding may be explained by the dissimilar performance of the older adults in the UL and LL tests to assess SMS. It should be noted that the UL- and LL-SMQ indexes are dissimilar in their nature due to the different characteristics of the SMS tests. The UL-SMQ index refers to a maximal isometric SMS normalized by SMM, which is commonly reported in the literature [6]. The LL-SMQ index refers to maximal performance on an LL functional task normalized by SMM, in which its ability seems to not be exclusively dependent of the maximal dynamic LL-SMS. Although the 30-s CST shows a high correlation with one-repetition maximum test on the leg press (older females: r = 0.71; older males: r = 0.78) [23], which is a multi-joint exercise involving the hips, knees, and ankles, it seems reasonable to assume the 30-s CST performance requires additional fitness-related components in addition to maximal dynamic SMS, such as dynamic balance, coordination, and power. We believe that the above-mentioned aspects may explain the fair agreement between UL and LL measures to identify low SMQ in older adults.

From a clinical perspective, our findings might be useful to rethink the recommendation of the interchangeable use of the HGS and CST in the EWGSOP2 [7] practical algorithm for dynapenia and sarcopenia case-finding, diagnosis and severity, mainly due the limited agreement between these UL and LL tests to identify low SMS and SMQ in older adults. Based on our findings, using the HGS an older adult can be classified as ‘normal SMS’ and nonsarcopenic while using CST his/her classification can be dynapenia (low SMS) or even sarcopenia. The opposite scenario is also possible; i.e. ‘normal SMS’ and nonsarcopenic using CST and dynapenia or sarcopenia using HGS. Thus, misinterpretation regarding the clinical identification of dynapenia and sarcopenia can occur, which can favor unappropriated interventions delivered for these individuals.

Despite our novel and interesting findings, this study has limitations which should be mentioned. First, although HGS and 30-s CST are well recommended to assess SMS in older adults by clinical guidelines, including the EWGSOP2 [7], these tests have different characteristics which may have influenced our findings. Future studies could consider investigating the agreement between UL and LL measures to identify older adults with low SMS using tests with similar characteristics. Second, the cut-offs to determine low SMS, SMM and SMQ were defined according to sex and 10-year age groups due to a low number of participants aged 75–80 years. Other studies suggest determining neuromuscular characteristics and fitness-related performance cut-offs for 5-year age groups [23, 27]. Third, our study included older adults aged 60–80 years. Therefore, our findings should be interpreted with caution and they are not transferable to older adults aged > 80 years. Fourth, we recruited community-dwelling older adults by diverse advertisement methods, but we do not rule out the possibility of some selection bias due to the need for transportation to the research laboratory. This aspect might have limited the participation of older adults with poor mobility and other age-related conditions such as sarcopenia and frailty. Fifth, as previously described, the DXA technique assesses the LST, which includes SMM and other body composition components [24, 25]. Although LST is highly correlated with SMM assessed by magnetic resonance imaging and computerized tomography imaging [24] and is a well-recognized proxy of SMM, the DXA technique does not provide a specific evaluation of SMM.

5 Conclusion

The agreement between UL and LL measures to identify low SMS, SMM and SMQ in older adults is limited, which might generate different clinical interpretations for diagnosing sarcopenia cases. In order to establish better implications of our findings, it seems important to identify which neuromuscular UL or LL measure (SMS, SMM and SMQ) is more associated with adverse health-related outcomes in older adults. Future studies to address the above-mentioned question are important.

References

  1. 1. Lynch NA, Metter EJ, Lindle RS, Fozard JL, Tobin JD, Roy TA, et al. Muscle quality. I. Age-associated differences between arm and leg muscle groups. J Appl Physiol. 1999;86: 188–194. pmid:9887130
  2. 2. Tracy BL, Ivey FM, Hurlbut D, Martel GF, Lemmer JT, Siegel EL, et al. Muscle quality. II. Effects of strength training in 65- to 75-yr-old men and women. J Appl Physiol. 1999;86: 195–201. pmid:9887131
  3. 3. Metter EJ, Lynch N, Conwit R, Lindle R, Tobin J, Hurley B. Muscle quality and age: Cross-sectional and longitudinal comparisons. Journals Gerontol—Ser A Biol Sci Med Sci. 1999;54: 8207–8218. pmid:10362000
  4. 4. Frontera WR, Hughes VA, Lutz KJ, Evans WJ. A cross-sectional study of muscle strength and mass in 45- to 78-yr-old men and women. J Appl Physiol. 1991;71: 644–650. pmid:1938738
  5. 5. Barbat-Artigas S, Rolland Y, Zamboni M, Aubertin-Leheudre M. How to assess functional status: A new muscle quality index. J Nutr Heal Aging. 2012;16: 67–77. pmid:22238004
  6. 6. Fragala MS, Kenny AM, Kuchel GA. Muscle Quality in Aging: a Multi-Dimensional Approach to Muscle Functioning with Applications for Treatment. Sport Med. 2015;45: 641–658. pmid:25655372
  7. 7. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing. 2019;48: 16–31. pmid:30312372
  8. 8. Zoico E, Di Francesco V, Guralnik JM, Mazzali G, Bortolani A, Guariento S, et al. Physical disability and muscular strength in relation to obesity and different body composition indexes in a sample of healthy elderly women. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 2004;28: 234–241. pmid:14708033
  9. 9. Xue QL. The Frailty Syndrome: Definition and Natural History. Clin Geriatr Med. 2011;27: 1–15. pmid:21093718
  10. 10. Berry SD, Miller RR. Falls: Epidemiology, pathophysiology, and relationship to fracture. Curr Osteoporos Rep. 2008;6: 149–154. pmid:19032925
  11. 11. Derksen JWG, Kurk SA, Peeters PHM, Dorresteijn B, Jourdan M, van der Velden AMT, et al. The association between changes in muscle mass and quality of life in patients with metastatic colorectal cancer. J Cachexia Sarcopenia Muscle. 2020;11: 919–928. pmid:32107889
  12. 12. Bye A, Sjøblom B, Wentzel-Larsen T, Grønberg BH, Baracos VE, Hjermstad MJ, et al. Muscle mass and association to quality of life in non-small cell lung cancer patients. J Cachexia Sarcopenia Muscle. 2017;8: 759–767. pmid:28493418
  13. 13. Farmer RE, Mathur R, Schmidt AF, Bhaskaran K, Fatemifar G, Eastwood S V., et al. Associations between measures of sarcopenic obesity and risk of cardiovascular disease and mortality: A cohort study and mendelian randomization analysis using the UK biobank. J Am Heart Assoc. 2019;8. pmid:31221000
  14. 14. Brown JC, Harhay MO, Harhay MN. The muscle quality index and mortality among males and females. Ann Epidemiol. 2016;26: 648–653. pmid:27480478
  15. 15. Reinders I, Murphy RA, Brouwer IA, Visser M, Launer L, Siggeirsdottir K, et al. Muscle Quality and Myosteatosis: Novel Associations with Mortality Risk. Am J Epidemiol. 2016;183: 53–60. pmid:26643983
  16. 16. Newman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, et al. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. Journals Gerontol—Ser A Biol Sci Med Sci. 2006;61: 72–77. pmid:16456196
  17. 17. Kemmler W, von Stengel S, Schoene D. Longitudinal Changes in Muscle Mass and Function in Older Men at Increased Risk for Sarcopenia—The FrOST-Study. J frailty aging. 2019;8: 57–61. pmid:30997916
  18. 18. Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, et al. Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: The Health, Aging and Body Composition Study. J Am Geriatr Soc. 2002;50: 897–904. pmid:12028178
  19. 19. Rantanen T, Masaki K, Foley D, Izmirlian G, White L, Guralnik JM. Grip strength changes over 27 yr in Japanese-American men. J Appl Physiol. 1998;85: 2047–2053. pmid:9843525
  20. 20. Johansson J, Strand BH, Morseth B, Hopstock LA, Grimsgaard S. Differences in sarcopenia prevalence between upper-body and lower-body based EWGSOP2 muscle strength criteria: the Tromsø study 2015–2016. BMC Geriatr. 2020;20: 1–11. pmid:33172391
  21. 21. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61: 344–349. pmid:18313558
  22. 22. Coldham F, Lewis J, Lee H. The reliability of one vs. three grip trials in symptomatic and asymptomatic subjects. J Hand Ther. 2006;19: 318–327.
  23. 23. Rikli RE, Jones CJ. Development and validation of a functional fitness test for community-residing older adults. J Aging Phys Act. 1999;7: 129–161.
  24. 24. Buckinx F, Landi F, Cesari M, Fielding RA, Visser M, Engelke K, et al. Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle. 2018;9: 269–278. pmid:29349935
  25. 25. Prado CMM, Heymsfield SB. Lean tissue imaging: a new era for nutritional assessment and intervention. JPEN J Parenter Enteral Nutr. 2014;38: 940–953. pmid:25239112
  26. 26. Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS One. 2009;4: e7038. pmid:19753111
  27. 27. Rikli RE, Jones CJ. Functional Fitness Normative Scores for Community-Residing Older Adults, ages 60–94. J Aging Phys Act. 1999;7: 162–184.
  28. 28. Lustosa LP, Pereira DS, Dias RC, Britto RR, Parentoni AN, Souza L, et al. Tradução e adaptação transcultural do Minnesota Leisure Time Activities Questionnaire em idosos. Geriatr Gerontol. 2000;2011: 57–65.
  29. 29. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43: 1334–1359. pmid:21694556
  30. 30. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33: 159–174. pmid:843571
  31. 31. Hughes VA, Frontera WR, Wood M, Evans WJ, Dallal GE, Roubenoff R, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci. 2001;56: B209–17. pmid:11320101
  32. 32. Candow DG, Chilibeck PD. Differences in size, strength, and power of upper and lower body muscle groups in young and older men. J Gerontol A Biol Sci Med Sci. 2005;60: 148–156. pmid:15814855
  33. 33. Frontera WR, Hughes VA, Fielding RA, Fiatarone MA, Evans WJ, Roubenoff R. Aging of skeletal muscle: A 12-yr longitudinal study. J Appl Physiol. 2000;88: 1321–1326. pmid:10749826
  34. 34. Winegard KJ, Hicks AL, Sale DG, Vandervoort AA. A 12-year follow-up study of ankle muscle function in older adults. Journals Gerontol—Ser A Biol Sci Med Sci. 1996;51: 202–207. pmid:8630696
  35. 35. Yeung SSY, Reijnierse EM, Trappenburg MC, Hogrel J-Y, McPhee JS, Piasecki M, et al. Handgrip Strength Cannot Be Assumed a Proxy for Overall Muscle Strength. J Am Med Dir Assoc. 2018;19: 703–709. pmid:29935982
  36. 36. Bohannon RW. Measurement of sit-to-stand among older adults. Top Geriatr Rehabil. 2012;28: 11–16.
  37. 37. McCormick R, Vasilaki A. Age-related changes in skeletal muscle: changes to life-style as a therapy. Biogerontology. 2018;19: 519–536. pmid:30259289
  38. 38. Keller K, Engelhardt M. Strength and muscle mass loss with aging process. Age and strength loss. Muscles Ligaments Tendons J. 2013;3: 346–350. pmid:24596700
  39. 39. Ata AM, Kara M, Kaymak B, Özçakar L. Sarcopenia Is Not “love”: You Have to Look Where You Lost it! Am J Phys Med Rehabil. 2020;99: e119–e120. pmid:32084033
  40. 40. Mitchell WK, Williams J, Atherton P, Larvin M, Lund J, Narici M. Sarcopenia, dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review. Front Physiol. 2012;3 JUL: 1–18. pmid:22275902
  41. 41. Bhasin S, Travison TG, Manini TM, Patel S, Pencina KM, Fielding RA, et al. Sarcopenia Definition: The Position Statements of the Sarcopenia Definition and Outcomes Consortium. J Am Geriatr Soc. 2020;68: 1410–1418. pmid:32150289
  42. 42. Fragala MS, Fukuda DH, Stout JR, Townsend JR, Emerson NS, Boone CH, et al. Muscle quality index improves with resistance exercise training in older adults. Exp Gerontol. 2014;53: 1–6. pmid:24508922