Osteoporosis International

, Volume 28, Issue 1, pp 59–70 | Cite as

Strength measures are better than muscle mass measures in predicting health-related outcomes in older people: time to abandon the term sarcopenia?

  • J. C. Menant
  • F. Weber
  • J. Lo
  • D. L. Sturnieks
  • J. C. Close
  • P. S. Sachdev
  • H. Brodaty
  • S. R. Lord
Original Article



There is no clear consensus on definition, cut-points or standardised assessments of sarcopenia. We found a lower limb strength assessment was at least as effective in predicting balance, mobility and falls in 419 older people as muscle mass-based measures of sarcopenia.


There is currently no consensus on the definition, cut-points or standardised assessments of sarcopenia. This study aimed to investigate whether several published definitions of sarcopenia differentiate between older people with respect to important functional and health outcomes.


Four hundred nineteen community-living older adults (mean age 81.2 ± 4.5, 49 % female) completed assessments of body composition (dual-energy X-ray absorptiometry), strength, balance, mobility and disability. Falls were recorded prospectively for a year using monthly calendars. Sarcopenia was defined according to four skeletal mass-based definitions, two strength-based definitions (handgrip or knee extensor force) and a consensus algorithm (low mass and low strength or slow gait speed). Obesity was defined according to percentage fat mass or waist circumference.


The four skeletal mass-based definitions varied considerably with respect to the percentage of participants classified as sarcopenic and their predictive accuracy for functional and health outcomes. The knee extension strength-based definition was equivalent to or better than the mass-based and consensus algorithm definitions; i.e. weaker participants performed poorly in tests of leaning balance, stepping reaction time, gait speed and mobility. They also had higher physiological fall risk scores and were 43 % more likely to fall at home than their stronger counterparts. Adding obesity to sarcopenia definitions identified participants with greater self-reported disability.


A simple lower limb strength assessment was at least as effective in predicting balance, functional mobility and falls in older people as more expensive and time-consuming muscle mass-based measures. These findings imply that functional terms such as muscle weakness or motor impairment are preferable to sarcopenia.


Accidental falls Aged Balance Mobility Muscle strength Obesity Sarcopenia 


Etymologically, the term ‘sarcopenia’ comes from ‘sarco-’, the Greek word for ‘flesh’ (muscle), and ‘-penia’ which denotes ‘deficiency’. The original definition reflected this as ‘the degenerative loss of skeletal muscle mass that accompanies ageing (0.5–1 % loss per year after the age of 25)’ [1]. It was inspired by the well-defined and documented term, osteoporosis, a decline in bone mass linked to an individual’s risk of experiencing a fracture. Given its implication for clinical practice, attempts have been made to link the term sarcopenia with physical outcomes in older adults [1].

Some studies have reported significant associations between sarcopenia—defined as reduced muscle circumference from computed tomography measurements or as low muscle mass from dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance measurements—with outcome measures including reduced grip strength [2], impaired mobility [3, 4, 5, 6], poor balance and increased number of previous falls [4]. In contrast, others have found no significant relationships between these anthropometry-based measures of sarcopenia and self-reported disability [7], gait speed and single-leg stance performance [8], performance in the Timed Up and Go and five chair rise tests [9], isometric leg extension strength [9], falls in the past year, 4-year fracture incidence or mortality in men and women [2]. Combining sarcopenia with obesity has also led to conflicting results. Some studies have found sarcopenic obesity to be associated with and precede the onset of incidental activities of daily living disability in older people [10] while others have reported that obesity contributes more to reduced physical capacity (on a range of functional mobility tests) than sarcopenia [11].

Other studies have shown body composition assumed by muscle weakness might be a more consistent predictor of health outcomes than muscle mass in older people. A combination of low muscle strength and obesity (dynapenic obesity) was predictive of increased fall risk (composite measure from 5 tests of physiological function) in 674 middle-aged and older adults whereas sarcopenic obesity was not [12]. Dynapenic obesity was also associated with lower walking speed compared with obesity alone [13, 14], an increased risk of developing mobility disability, especially among older persons aged between 65 and 80 years [14], and was an independent risk factor for all-cause mortality during a 5-year follow-up in a large sample of older Brazilians [15].

Overall, despite a large body of literature, there is still no clear consensus on definition, external validity of cut-off values or standardised assessments of sarcopenia. These significant limitations present a substantial impediment to clinical use of sarcopenia and also likely explain the large variation in sarcopenia prevalence reported across studies of older people: between 10 and 90 % [16]. The broadening of the sarcopenia definition by the European Working Group on Sarcopenia in Older People (EWGSOP) to include loss of muscle strength or low functional performance in addition to low muscle mass [17] reflects mobility or motor impairment and is not too dissimilar to the multidimensional construct of frailty, widely used in clinical practice [18]. However, using that sarcopenia definition, a study of 409 home-dwelling Finish older women aged 70–80 reported a 0.9 % of sarcopenia prevalence [9]. Several studies [18, 19, 20] have identified the need for more research to address the shortcoming of the definition of sarcopenia and to pave the way for a consistent and clear approach to operationalising the construct in clinical practice. In addition, whether the relationship between obesity and falls reported in an epidemiological study [21] may be partly attributed to sarcopenic obesity is unclear.

The aim of this prospective cohort study was to compare the discriminative ability of several measures of sarcopenia (lean body mass and strength derived) with respect to health-related outcomes in community-dwelling older people. We selected a range of important outcome measures on a continuum ranging from basic objective assessments of sensorimotor function, balance control and stepping through clinical measures of gait and general mobility to self-reported falls and disability in performing activities of daily living. Good performance in any of these measures is critical to maintaining a quality of life and independence in old age. Secondary aims were to explore whether (i) the inclusion of obesity, and (ii) using the algorithm of the EWGSOP, improves the discriminative ability with regard to the selected outcomes.



Participants were recruited from a cohort of 792 community-dwelling older people living in Eastern Sydney and participating in Wave 3 of the Sydney Memory and Ageing Study (age range 74–94 years) and identified through the electoral roll. The detailed methodology of the Sydney Memory and Ageing Study has been reported elsewhere [22]. In brief, all participants had a Mini-Mental State Examination (MMSE) score of 24 or greater [23], were independent in activities of daily living and were able to walk 400 m without assistance. The exclusion criteria for SMAS were a previous diagnosis of dementia or developmental disability, psychotic symptoms, Parkinson’s disease, multiple sclerosis, motor neuron disease or central nervous system inflammation or if they had medical or psychological conditions that may have prevented them from completing assessments. Participants who weighed over 120 kg or had undergone nuclear medicine or barium studies or medical examination that also included dye or contrast material in the past week were also excluded from the study as they could not undertake the DXA assessment. Body composition and fall risk assessments were performed in a subsample of 419 participants. The Human Research Ethics Committee at the University of New South Wales approved the protocol, and informed consent was obtained from all participants.


Demographic characteristics, medical history and physical activity

Participants completed questionnaires pertaining to demographic information, fall history, medical conditions and planned physical activity.

Body composition measures

DXA was used to measure body composition. A whole-body scan with the participant lying supine on the DXA scanner bed (Lunar DPX-NT scanner) was obtained, according to the manufacturer’s guidelines. The DXA system differentiates bone, muscle and fat and calculates total body mass, fat mass, percentage fat and lean body mass, as well as the regional distribution of these components (left arm, leg and trunk; right arm, leg and trunk; total arm, leg and trunk). The appendicular skeletal mass index was determined by calculating appendicular skeletal mass (sum of lean mass in the arms and legs in kg), divided by height (m), and squared [6]. DXA percentage body fat was determined as total fat mass divided by total body mass and multiplied by 100. If the participant’s body did not fit within the scan space, a half-scan analysis was automatically performed, by assuming symmetry of the body. DXA has been validated as a method of assessing these body composition measures in younger and older persons [24] with good reported reproducibility and sensitivity to small differences in body composition [25]. Based on repeated scans of 10 healthy adults (age range 22–39 years; 3 males, 7 females) with repositioning, the test-retest reliability (intraclass correlation coefficients (ICC) with 95 % confidence intervals (95 % CI)) and in vivo precision (coefficient of variation, CV %) of the DXA measures of interest were as follows: arms lean mass (ICC (95%CI): 0.994 (0.977–0.999), p < 0.001, CV = 1.72 %; legs lean mass (ICC (95%CI): 0.988 (0.951–0.997), p < 0.001, CV = 2.43 %; total fat mass (ICC (95%CI): 0.993 (0.971–0.998), p < 0.001, CV = 3.75 %).

Height and weight were measured and used to compute body mass index (BMI) as weight (kg) divided by height (m) squared. Waist circumference was measured to the nearest 0.1 cm at the centre point between the lower rib margin and the iliac crest.

Strength measures

Handgrip strength was measured using a handheld dynamometer (SH5001, SAEHAN Corporation, Changwon, Korea) and the best performance of three trials on each hand was recorded. Knee extension strength was measured on the dominant leg as the maximal (from three trials) isometric knee extension force (kg) with participants seated, knee flexed to 90° using an electronic strain gauge attached to the lower leg.

Definitions of sarcopenia and obesity

Sarcopenia and obesity definitions (Table 1) were extracted from previously published work investigating associations between body composition and functional and health-related outcome variables [6, 10, 11, 12, 17, 26, 27]. Initially, four anthropometric (based on appendicular skeletal mass: Levine and Crimmins [26]; Baumgartner et al. [10]; Scott et al. [12] and Bouchard et al. [11]), two clinical (based on upper or lower limb strength) and one mixed (consensus from the EWGSOP [17]) definitions of sarcopenia were examined. The anthropometric and clinical definitions with the best discriminative ability with respect to the investigated outcome measures were subsequently combined with an anthropometric (based on % body fat) or clinical (based on waist circumference) obesity criteria to categorise participants into one of four body composition phenotypes: normal lean (non-sarcopenic, non-obese), sarcopenic, obese and sarcopenic obese.
Table 1

Definitions and cut-off values for sarcopenia and obesity based on DXA or clinical measurements

Author, year



DXA-based definitions

Levine and Crimmins, 2012 [24]

Appendicular skeletal (lean) mass (as % total body mass)

<25.72 % for males; <19.43 % for females


Scott et al., 2014 [11]

Bottom tertile of the residuals from the regression of appendicular lean mass (g) on height (m) and fat mass (g)

<326.4 for males; ≤2217.8 for females


Baumgartner et al., 2004 [9]

Appendicular skeletal mass index

<7.26 kg/m2 for males; <5.45 kg/m2 for females


Bouchard et al., 2009 [8]

Appendicular skeletal mass index

<8.51 kg/m2 for males; <6.29 kg/m2 for females

≥28 % body fat for males and ≥35 % for females

Functional definitions

Knee extension strength-based

Lowest sex-specific quintile of knee extension strength

<23.64 kg for males; <15.24 kg for females

Waist circumference: >102 cm for males; >88 cm for females

Handgrip strength-based

Cut-points from the European Working Group on Sarcopenia in older people <30.00 kg for males; <20.00 kg for females


Fall risk and other physical function assessments

Participants underwent the short-form Physiological Profile Assessment (PPA) which comprises five sensorimotor tests evaluating key functions of the human balance system: peripheral sensation, visual contrast sensitivity, knee extension strength, simple hand reaction time and postural sway. Descriptions of the apparatus, procedures and test-retest reliability for these tests are reported elsewhere [28]. This fall risk assessment has been shown to predict risk of recurrent falls in community-dwelling older people with an accuracy up to 75 % [29].

In addition, participants performed a test of leaning balance, the coordinated stability task, which measures participants’ ability to adjust balance in a steady and coordinated way while placing them near or at the limits of their base of support [30]. This test uses a simple device comprising a 40-cm rod which is attached to participants at waist level by a firm belt. The participant is asked to adjust balance by bending or rotating the body without moving the feet, so that a pen mounted vertically at the end of the rod follows and remains within a convoluted track which is marked on a piece of paper attached to the top of an adjustable height table. A total error score is calculated by scoring the number of occasions that the pen fails to stay within the path. Participants complete a practice trial before completing the test. Participants also completed the choice stepping reaction time test (CSRT), a composite fall risk measure, which determines the time required for participants to take an anterior or lateral step in response to a light stimulus [31]. Gait speed (m/s) was calculated from the 6-m-walk test performed at self-selected walking speed, using the middle 6 m of a 10-m-long walkway. Participants performed the Timed Up and Go (TUG) (s) at fast pace; it required standing up from a chair with armrests, walking 3 m, turning around, walking 3 m back and sitting down [32].


Participants completed the 12-item World Health Organization Disability Assessment Schedule (WHODAS II) [33] which assesses levels of functioning in cognition, mobility, self-care, social interactions, life activities and participation in society. A higher score on the WHODAS II indicates increased level of disability.


A fall was defined as an unexpected event in which a person comes to rest on the ground, floor or other lower level [34]. Fall frequency during the 12 months following assessment was monitored with monthly fall diaries and follow-up telephone calls as required [34]. Participants returning all fall calendars or who had at least one fall during the follow-up period were included in the analysis. Fallers were defined as those who experienced at least one fall in the follow-up period. The sub-group of fallers who fell at home were also contrasted with the remainder of participants as it has been found that indoor fallers suffer high rates of future immobility [35].

Statistical analysis

Data were analysed using SPSS Version 22 for Windows (IBM Corp, Armonk, NY). To permit parametric analyses, data with right-skewed distributions were square root or log transformed. Independent sample t tests and one-way analyses of variance (ANOVAs) were used to compare means of continuous dependent variables between groups (gender; sarcopenic vs. non-sarcopenic; body composition phenotypes). When a significant group main effect was identified in the ANOVAs, post hoc tests with Bonferroni corrections for multiple comparisons were performed. Chi-square tests from contingency tables were used to compare the prevalence of medical conditions between genders. Associations between sarcopenia or combined sarcopenia and obesity status and falling status were examined using the relative risks (RR) statistic.

All missing data were reviewed; data missing completely at random (<10 % for a specific variable and due to equipment problems) were estimated and replaced using the expectation–maximisation (EM) algorithm within SPSS version 22. For data missing not at random (participant not able to complete an assessment due to physical frailty), missing data were replaced with the value of group mean ± three standard deviations (SD).


Baseline characteristics

Baseline characteristics are presented in Table 2. Compared with men, women were significantly younger, had shorter stature, lower body weight, higher total body fat mass, lower total lean body mass and a lower ASM index. Women performed significantly worse than men in tests of hand grip and knee extensor strength, leaning balance and choice stepping reaction time. Arthritis was more prevalent in women whereas heart disease, diabetes mellitus and stroke were significantly more prevalent in men.
Table 2

Demographics, body composition measures, functional measurements and health-related factors for the whole sample and sex-specific comparisons



n = 419


n = 207


n = 212


Age, years

81.2 ± 4.5

80.6 ± 4.3*

81.9 ± 4.5

Height, cm

163.1 ± 8.9

156.8 ± 6.5*

169.1 ± 6.5

Weight, kg

71.6 ± 13.3

65.7 ± 11.4*

77.7 ± 12.5

BMI, kg/m2

26.9 ± 4.3

26.8 ± 4.7

27.1 ± 3.7

MMSE scorea

29.0 ± 1.4

29.1 ± 1.3

29.0 ± 1.4

Education, years

13.6 ± 4.2

12.9 ± 3.9*

14.4 ± 4.3

Body composition

Total body fat mass, kg

24.2 ± 8.9

25.8 ± 9.4*

22.6 ± 8.2

Total body lean mass, kg

43.0 ± 9.0

36.0 ± 4.3*

50.0 ± 6.6

Appendicular skeletal mass index, kg/m2

6.9 ± 1.1

6.2 ± 0.8*

7.7 ± 0.9

Waist circumference, cm

96.1 ± 11.6

91.6 ± 11.1*

100.5 ± 10.2

Functional measurements

Hand grip strength, kg (n = 314; n = 145 female)

26.6 ± 9.3

19.8 ± 5.3*

32.4 ± 7.9

Knee extension muscle strength, kg

26.1 ± 9.3

21.2 ± 6.5*

30.9 ± 9.2

PPA fall risk scoreb

0.5 ± 0.9

0.5 ± 0.9

0.5 ± 0.9

Coordinated stability

12.0 ± 11.1

13.9 ± 11.3*

10.1 ± 10.7

CSRT, msc

1164 ± 229

1189 ± 237*

1139 ± 219

Gait speed, m/s

0.7 ± 0.1

0.7 ± 0.1

0.7 ± 0.1

TUG, s

9.6 ± 2.7

9.6 ± 2.7

9.7 ± 2.8


Heart disease

69 (17 %)

23 (11 %)*

46 (22 %)

Diabetes mellitus

54 (13 %)

16 (8 %)*

38 (18 %)

Joint problems, rheumatism or arthritis

239 (57 %)

134 (65 %)*

105 (50 %)


15 (4 %)

1 (1 %)*

14 (7 %)

Planned physical activity, h/weekd

10.7 ± 8.8

10.1 ± 7.1

11.4 ± 10.1

WHODAS scoree

18.4 ± 6.1

18.4 ± 5.7

18.5 ± 6.4

Fallers (1+ falls)

194 (46 %)

98 (47 %)

96 (45 %)

At-home fallers

110 (26 %)

49 (24 %)

61 (29 %)

Data are presented as mean ± SD or n (%). Due to equipment unavailability, handgrip strength data were only collected for 314 participants

SD standard deviation

aMini Mental State Examination score [21], score range 0–30; higher score indicates better cognition

bPhysiological Profile Assessment score; a higher score indicates increased risk of falls

cChoice stepping reaction time

dFrom the Incidental and Planned Physical Exercise Questionnaire

e12-Item World Health Organization Disability Assessment Schedule

*Significantly different to males (p < 0.05)

DXA-based sarcopenia definitions and functional and health-related outcomes

Depending on the definition (Table 1), the following proportions of participants were classified as sarcopenic: Levine and Crimmins [26]—14 %, Baumgartner et al. [10]—23 %, Scott et al. [12]—33 % and Bouchard et al. [11]—73 %. Table 3 shows the outcome measures for sarcopenic and non-sarcopenic groups defined by the different DXA cut-points. There were no significant differences in PPA fall risk scores and CSRTs between the sarcopenic and non-sarcopenic groups for all sarcopenia definitions. The Levine and Crimmins definition [26] (Table 1) significantly discriminated between sarcopenic and non-sarcopenic groups for the greatest number of physical and health-related outcome measures: leaning balance, gait speed, TUG test time and WHODAS score.
Table 3

Comparisons of sarcopenic and normal lean groups according to a range of definitions based on DXA measurements and/or muscle strength measurements for all the outcome measures


DXA-based sarcopenia definitions

Functional definitions

Composite definition


Levine and Crimmins

Scott et al.

Baumgartner et al.

Bouchard et al.

Knee extension strength

Handgrip strength


Sarcopenic (n (%))

57 (14 %)

139 (33 %)

97 (23 %)

306 (73 %)

84 (20 %)

127 (40 %)

88 (22 %)

Non-sarcopenic (n (%))

362 (86 %)

280 (67 %)

322 (77 %)

113 (27 %)

335 (80 %)

187 (60 %)

322 (78 %)

Composite fall risk score (PPA)


0.40 ± 0.83

0.48 ± 0.88

0.60 ± 0.94

0.48 ± 0.89

0.73 ± 0.89*

0.46 ± 0.84

0.62 ± 0.92


0.47 ± 0.89

0.46 ± 0.88

0.42 ± 0.86

0.42 ± 0.85

0.40 ± 0.86

0.32 ± 0.82

0.43 ± 0.86

Coordinated stability scorea


14.7 ± 10.9*

10.7 ± 10.7

12.4 ± 12.1

11.7 ± 11.0

15.6 ± 12.7**

14.1 ± 11.6**

12.2 ± 12.0


11.6 ± 11.1

12.6 ± 11.3

11.8 ± 10.8

12.9 ± 11.5

11.1 ± 10.5

8.9 ± 9.5

12.0 ± 10.9

Choice stepping reaction time (ms)b


1177 ± 195

1160 ± 235

1189 ± 244

1166 ± 253

1271 ± 252**

1191 ± 247**

1202 ± 247


1162 ± 234

1165 ± 226

1156 ± 224

1159 ± 219

1137 ± 215

1099 ± 190

1152 ± 222

6-m walk test gait speed (m s−1)c


0.62 ± 0.14**

0.67 ± 0.15

0.65 ± 0.14*

0.67 ± 0.14

0.63 ± 0.14**

0.65 ± 0.14**

0.63 ± 0.12*


0.69 ± 0.15

0.68 ± 0.15

0.69 ± 0.15

0.69 ± 0.16

0.69 ± 0.15

0.73 ± 0.15

0.69 ± 0.15

Timed Up and Go test time (s)d


11.2 ± 3.2**

10.0 ± 2.8

10.1 ± 2.9

9.7 ± 2.7

10.8 ± 3.3**

10.1 ± 2.7**

10.3 ± 3.0*


9.4 ± 2.6

9.5 ± 2.7

9.5 ± 2.7

9.6 ± 2.7

9.4 ± 2.5

8.7 ± 2.3

9.5 ± 2.7

WHODAS scoree


20.5 ± 6.2**

18.3 ± 5.4

18.3 ± 5.2

18.5 ± 6.2

19.2 ± 5.7

18.5 ± 5.5*

18.4 ± 5.2


18.1 ± 6.0

18.5 ± 6.4

18.4 ± 6.3

18.1 ± 5.6

18.2 ± 6.2

17.1 ± 5.7

18.4 ± 6.3

Fallers (1+ fall)


24 (42 %)

74 (53 %)*

56 (58 %)*

144 (47 %)

46 (55 %)

61 (48 %)

49 (56 %)*


170 (47 %)

120 (43 %)

138 (43 %)

50 (44 %)

148 (44 %)

89 (48 %)

142 (44 %)

At-home fallers


17 (30 %)

45 (32 %)*

36 (37 %)**

81 (27 %)

29 (35 %)*

33 (26 %)

34 (39 %)**


93 (26 %)

65 (23 %)

74 (23 %)

29 (26 %)

81 (24 %)

52 (28 %)

74 (23 %)

Data are presented as mean (±SD) or n (%). Higher scores in the tests of PPA fall risk, coordinated stability, choice stepping reaction time, Timed Up and Go and WHODAS assessments indicate worse performances. Available data for the sarcopenia measures: EWGSOP definition (n = 410), hand grip strength (n = 314). Data collected for outcome measures: coordinated stability (n = 409); choice stepping reaction time (n = 403); Gait speed (n = 378); Timed Up and Go Test (n = 409), WHODAS (n = 409); data imputed for the analyses.

*Significant difference between sarcopenic and non-sarcopenic; p < 0.05

**Significant difference between sarcopenic and non-sarcopenic; p < 0.01

There were significantly more fallers and at-home fallers in the sarcopenic group compared with the non-sarcopenic group, when sarcopenia was defined according to Scott et al. [12] (fallers: RR (95 % CI) = 1.24 (1.01–1.53); at-home fallers: RR (95 % CI) = 1.39 (1.01–1.92)) and Baumgartner et al. [10] (fallers: RR (95 % CI) = 1.35 (1.09–1.67); at-home fallers: RR (95%CI) = 1.62 (1.16–2.24)).

Strength-based sarcopenia definitions and functional and health-related outcomes

As shown in Table 3, the knee extension strength sarcopenic participants (bottom quintile of the sample) performed significantly worse than the non-sarcopenic group in all sensorimotor and functional measures and were 43 % more likely to fall at home (RR (95 % CI) = 1.43 (1.01–2.03)). In contrast, the handgrip strength sarcopenic group performed worse than the non-sarcopenic group in the tests of coordinated stability, CSRT, gait speed and TUG and significantly discriminated between those with low and high disability scores.

EWGSOP-sarcopenia definition and functional and health-related outcomes

The algorithm developed by the EWGSOP identified 22 % of participants (n = 88) as sarcopenic (Fig. 1). The sarcopenic participants performed significantly worse than their non-sarcopenic counterparts in the tests of gait speed and TUG (Table 3). Furthermore, compared with the non-sarcopenic group, the sarcopenic group included a higher percentage of fallers (R = 1.26 (1.01–1.60)) and at-home fallers (RR = 1.68 (1.21–2.34)).
Fig. 1

Algorithm to diagnose sarcopenia developed by the European Working Group on Sarcopenia in Older People (EWGSOP) [16]. Low handgrip strength cut-off points: <30 kg for males; <20 kg for females. Low muscle mass cut-off points: Appendicular skeletal mass index: <7.2 kg/m2 for males; <5.5 kg/m2 for females

Including obesity to enhance discriminant ability for functional outcomes

Obesity measurement (% body fat based on Bouchard et al. cut-points [11] (Table 1)) was added to the best performing sarcopenia definition (Levine and Crimmins [26]) to investigate if this improved discriminant ability for functional outcomes. This classified 33 % (n = 140) of participants as normal lean, 0.2 % (n = 1) as sarcopenic, 13 % (n = 56) as sarcopenic obese and 53 % (n = 222) as non-sarcopenic obese. One-way ANOVA (excluding the sarcopenic group as n = 1) revealed significant main effects of group for coordinated stability (F2, 417 = 4.58, p = 0.011), gait speed (F2,417 = 7.22, p = 0.002), TUG time (F2,374 = 10.06, p < 0.001) and WHODAS score (F2,401 = 5.09, p = 0.007), whereby the sarcopenic obese group performed significantly worse in those tests compared with the normal lean group (p < 0.005) (Table 4). The sarcopenic obese group also performed worse than the obese group in the tests of 6-m walk, TUG and in the WHODAS (p < 0.05) (Table 4). There were no significant differences in the proportion of fallers or at-home fallers between groups (Table 4).
Table 4

Comparisons of sarcopenic, normal lean, obese and sarcopenic obese groups defined according to either DXA-based or clinical measurements of sarcopenia and obesity for all the outcome measures


DXA measurements-based definition (% appendicular skeletal mass + % body fat)

Clinical measurements-based definition (knee strength + waist circumference)

Composite fall risk score (PPA)

Normal lean

0.56 ± 0.90

0.43 ± 0.988



0.74 ± 0.91


0.42 ± 0.87

0.37 ± 0.85

Sarcopenic obese

0.42 ± 0.83

0.72 ± 0.87

Coordinated stability score

Normal lean

10.4 ± 11.4&

9.3 ± 10.3$&



13.6 ± 12.9


12.3 ± 10.9

12.7 ± 10.5*

Sarcopenic obese

14.9 ± 10.9*

18.0 ± 12.2*

Choice stepping reaction time (ms)

Normal lean

1141 ± 234

1109 ± 217** &



1242 ± 247*


1175 ± 234

1164 ± 210&

Sarcopenic obese

1177 ± 197

1306 ± 256* $

6-m walk test gait speed (m s−1)

Normal lean

0.69 ± 0.14&

0.71 ± 0.14&



0.66 ± 0.14


0.69 ± 0.15&

0.67 ± 0.14&

Sarcopenic obese

0.61 ± 0.14*$

0.59 ± 0.14* $

Timed Up and Go test time (s)

Normal lean

9.4 ± 2.8&

9.0 ± 2.5$&



9.3 ± 2.3&


9.4 ± 2.5&

9.8 ± 2.5* &

Sarcopenic obese

11.2 ± 3.3* $

12.3 ± 3.5#

WHODAS score

Normal lean

17.7 ± 5.8&

17.7 ± 6.1&



17.6 ± 5.4&


18.3 ± 6.1&

18.7 ± 6.2

Sarcopenic obese

20.6 ± 6.2*$

21.1 ± 5.6* **

Fallers (1+ fall)

Normal lean

68 (49 %)

79 (48 %)


1 (100 %)

23 (50 %)


102 (46 %)

69 (40 %)

Sarcopenic obese

23 (41 %)

23 (61 %)$

At-home fallers

Normal lean

38 (27 %)

40 (24 %)


1 (100 %)

15 (33 %)


55 (25 %)

41 (24 %)

Sarcopenic obese

16 (29 %)

14 (37 %)

Data are presented as mean (±SD) or n (%). Higher scores in the tests of fall risk, coordinated stability, CSRT, TUG and WHODAS indicate worse performances

*Significantly different from the normal lean group, p < 0.05

**Significantly different from the sarcopenic group, p < 0.05

$Significantly different from the obese group, p < 0.05

&Significantly different from the sarcopenic obese group, p < 0.05

#Significantly different from the all other groups, p < 0.05

When combined with waist circumference, the clinical based sarcopenia definition based on knee strength classified 39 % (n = 164) of the participants as normal lean, 11 % (n = 46) as sarcopenic, 9 % (n = 38) as sarcopenic obese and 41 % (n = 171) as obese. One-way ANOVAs revealed significant main effects of group for all of the physical outcome measures (F range = 3.39 (falls risk score)—16.9 (TUG), p < 0.05) (Table 4). The sarcopenic obese group performed significantly worse than the three other groups in the TUG test (p < 0.05) and worse than the normal lean participants in the tests of coordinated stability, CSRT, 6-m walk and WHODAS (p < 0.05) as well as they reported poorer quality of life in the WHODAS compared with the sarcopenic participants (p < 0.05). Compared with the obese group, the sarcopenic obese group also performed worse in the tests of CSRT and 6-m walk (p < 0.05) and more likely to fall ((RR (95%CI) = 1.50 (1.10–2.05)).

Sensitivity analyses excluding the imputed data led to similar conclusions except in two cases: coordinated stability performance was worse in the sarcopenic compared with the non-sarcopenic groups (12.0 ± 10.7 vs. 9.8 ± 9.3, p = 0.04) according to the Levine and Crimmins definition, and 6-m walk test gait speed did not differ between the sarcopenic and non-sarcopenic groups (0.65 ± 0.15 vs. 0.69 ± 0.15, p = 0.06) according to the Baumgartner definition.


In our sample of community-living older people, we found the four skeletal mass-based definitions varied considerably with respect to the percentage of participants they classified as sarcopenic (ranging from 14 to 73 %) and their predictive accuracy for functional and health outcomes was mixed. Of the skeletal mass-based definitions, the Levine and Crimmins’ definition (i.e. appendicular skeletal mass <25.72 % for males and <19.43 % for females) [26] performed best at revealing differences between sarcopenic and non-sarcopenic sub-groups for the tests of leaning balance, gait speed, TUG test time and WHODAS score. However, the simple knee extension strength-based definition performed better; i.e. compared with their stronger counterparts, those with weak knee extensors performed worse in the physical performance tests measuring leaning balance, stepping reaction time, gait speed and TUG, had higher PPA fall risk scores and were 43 % more likely to fall at home in the year following assessment.

Previously, Clark and Manini have warned that the use of the term sarcopenia to describe age-related loss of skeletal muscle mass and strength is erroneous as it implies ‘changes in skeletal muscle mass are directly and fully responsible for changes in strength’ [36]. However, it is well established that muscle mass loss is only one of many neuromuscular contributors to muscle weakness [36]. For example, declines in muscle mass estimated with urine creatinine concentration only contributed to 5 % of the variance in knee flexor and extensor musculature strength decline over a 10-year follow-up [37]. In addition, there is evidence that muscle mass declines earlier and at a much slower rate than muscle strength (approximately 1 % per year vs. 3–4 % per year) and that gains in lean mass do not transfer to proportional gains in muscle strength [38]. Neuromuscular factors, including apoptosis re-innervation and reorganisation of motor units, incomplete motor unit recruitment and increased antagonist activation, may contribute to the imperfect associations between muscle mass and muscle strength [39].

Another reason for the relatively poor associations between muscle mass-based sarcopenia definitions and functional outcomes reported here and in previous studies, such as in the Finnish DEX study [9], is that the most commonly used measure (DXA scan) does not assess muscle quality or separately account for intramuscular fat. Cross-sectional studies of older adults have reported that thigh intramuscular adipose tissue is associated with lower limb performance in older adults [5] and that the extent of adipose tissue was more strongly related to mobility performance than lean mass [40]. Furthermore, recent findings that older fallers exhibit higher intramuscular adipose tissue in lower limb muscles and a lower peak hip abduction torque than non-fallers [41] suggest that the lean mass composition of critical muscles involved in the control of balance, such as proximal gluteal muscles of the hip joint, is a risk factor for falls in older people.

As a proxy for sarcopenic obesity, we investigated whether combining measures of obesity using waist circumference with sarcopenia definitions enhances the prediction of health-related outcomes, as reported in previous studies [12, 13, 14]. In our sample, this approach did not improve the discrimination of most outcome measures but did identify those more likely to report greater levels of disability according to the WHODAS II. This finding is consistent with that from the New Mexico Elder Health Survey where older people with sarcopenic obesity at baseline were two to three times more likely to report onset of disability during an 8-year follow-up compared to their sarcopenic only, obese only or healthy counterparts [10].

Clinical implications

To improve its utility, the EWGSOP definition of sarcopenia was broadened to encompass multiple domains including muscle strength and gait speed. By doing so, it has migrated from its initial derivation (muscle deficiency), to encompassing functional measures and is more akin to another established construct, frailty. Frailty is a well described syndrome, albeit with more than one definition, with demonstrated relationships with negative health-related events including falls, hospitalization, worsening disability and mortality [20]. Further, despite its complexity, the EWGSOP definition performed worse in predicting physical and health-related outcomes than the classification of people into weak and strong groups based on a simple strength test.

Unless it is demonstrated that a muscle mass or quality measure is superior to a strength measure, we suggest the term muscle weakness should remain the preferred term over sarcopenia. Assessment of knee extension strength is indicated as it can be measured with a simple, inexpensive and reliable strain gauge in clinical practice, be used to identify at risk populations and is also sensitive to change over time thus can be used to evaluate. In addition, the knee extensors are a key muscle group involved in the ability to perform sit-to-stand transfers [42], maintain balance [43] and walk steadily [44]. In contrast, handgrip strength only relates to the hand and is a more general, indirect marker of immobility and disability [45]. For a more multi-domain approach, terms such as frailty or motor impairment more precisely capture the syndromes underlying impaired ability to perform activities of daily living, increased risk of falls and the loss of independence in older people. These can be assessed using simple assessment tools that are quick to administer, require minimal equipment and are already used in clinical practice [29, 46].

Strengths and limitations of the study

Strengths of the study include the large samples of both men and women, the prospective falls data and the broad assessment battery which included a comprehensive range of sarcopenia measurements and definitions. The study also has some limitations. First, the cross-sectional design of the study does not allow any causal relationships to be drawn. Although we found a functional strength measure correlates as well as or better than muscle mass measures with a range of balance, functional performance and quality of life measures in cross-sectional analyses, this is not proof of better clinical utility. However, these findings correspond well with a previous study that has made similar direct comparisons [9]. Second, as an agreed cut-point for poor knee extension strength is lacking, we used the bottom quintile scores from the men and women in our study. These cut-points are therefore optimal for our specific sample but are similar to those used in previous studies and the method is in line with the current research on this topic [47]. Third, the sample was relatively healthy, so the findings cannot be generalised to frail older people not living independently. Fourth, there was a substantial loss of data for the assessment of the handgrip strength as well as a large number of statistical tests which might have influenced the results. We also acknowledge that with respect to the important measure of prospectively determined falls, two of the four muscle mass measures were associated with all falls and at-home falls, whereas knee extension strength was associated only with at-home falls. However, at-home falls might be a more accurate indicator of frailty as previous studies have shown that indoor falls are associated with reduced physical functioning [48] and that indoor fallers suffer high rates of future immobility [35] and fall-related mortality [49]. Finally, given that knee extension strength is a component, albeit minor, of the PPA score, the association between these two measures may be slightly inflated.


A simple lower limb strength assessment was found to be at least as effective in predicting balance, functional mobility and falls in older people as more expensive and time-consuming muscle mass-based measures. These findings imply that compared to the term sarcopenia, more functional terms such as weakness, frailty or motor impairment more accurately reflect the syndrome whereby physiological and pathological changes directly impact on a persons’ ability to perform activities of daily living, maintain the upright posture and remain independent.



We are grateful to the many people who assisted with this study, including Melissa Brodie and the cohort of participants who volunteered their time.

Compliance with ethical standards


The participants in this study were drawn from the Memory and Ageing Study of the Brain and Ageing Program, School of Psychiatry, UNSW, funded by a NHMRC Program Grant (No. 350833) to Professors P. Sachdev, H. Brodaty and G. Andrews. This study was partly funded by an Early career researcher grant from the Faculty of Medicine at the University of New South Wales awarded in 2010 to J. Menant.

Conflicts of interest


Sponsor’s role



  1. 1.
    Rosenberg IH (1989) Summary comments. Am J Clin Nutr 50:1231–1233Google Scholar
  2. 2.
    Woo J, Leung J, Sham A, Kwok T (2009) Defining sarcopenia in terms of risk of physical limitations: a 5-year follow-up study of 3,153 Chinese men and women. J Am Geriatr Soc 57:2224–2231. doi:10.1111/j.1532-5415.2009.02566.x CrossRefPubMedGoogle Scholar
  3. 3.
    Reid KF, Naumova EN, Carabello RJ, Phillips EM, Fielding RA (2008) Lower extremity muscle mass predicts functional performance in mobility-limited elders. J Nutr Health Aging 12:493–498CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Szulc P, Beck TJ, Marchand F, Delmas PD (2005) Low skeletal muscle mass is associated with poor structural parameters of bone and impaired balance in elderly men—the MINOS study. J Bone Miner Res 20:721–729. doi:10.1359/JBMR.041230 CrossRefPubMedGoogle Scholar
  5. 5.
    Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E et al (2002) 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 50:897–904CrossRefPubMedGoogle Scholar
  6. 6.
    Waters DL, Hale L, Grant AM, Herbison P, Goulding A (2010) Osteoporosis and gait and balance disturbances in older sarcopenic obese New Zealanders. Osteoporos Int 21:351–357CrossRefPubMedGoogle Scholar
  7. 7.
    Visser M, Langlois J, Guralnik JM, Cauley JA, Kronmal RA, Robbins J et al (1998) High body fatness, but not low fat-free mass, predicts disability in older men and women: the Cardiovascular Health Study. Am J Clin Nutr 68:584–590PubMedGoogle Scholar
  8. 8.
    Bouchard DR, Beliaeff S, Dionne IJ, Brochu M (2007) Fat mass but not fat-free mass is related to physical capacity in well-functioning older individuals: nutrition as a determinant of successful aging (NuAge)—the Quebec Longitudinal Study. J Gerontol A Biol Sci Med Sci 62:1382–1388CrossRefPubMedGoogle Scholar
  9. 9.
    Patil R, Uusi-Rasi K, Pasanen M, Kannus P, Karinkanta S, Sievanen H (2013) Sarcopenia and osteopenia among 70-80-year-old home-dwelling Finnish women: prevalence and association with functional performance. Osteoporos Int 24:787–796. doi:10.1007/s00198-012-2046-2 CrossRefPubMedGoogle Scholar
  10. 10.
    Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE (2004) Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res 12:1995–2004. doi:10.1038/oby.2004.250 CrossRefPubMedGoogle Scholar
  11. 11.
    Bouchard DR, Dionne IJ, Brochu M (2009) Sarcopenic/obesity and physical capacity in older men and women: data from the Nutrition as a Determinant of Successful Aging (NuAge)-the Quebec longitudinal Study. Obesity 17:2082–2088. doi:10.1038/oby.2009.109 CrossRefPubMedGoogle Scholar
  12. 12.
    Scott D, Sanders KM, Aitken D, Hayes A, Ebeling PR, Jones G (2014) Sarcopenic obesity and dynapenic obesity: 5-year associations with falls risk in middle-aged and older adults. Obesity 22:1568–1574. doi:10.1002/oby.20734 CrossRefPubMedGoogle Scholar
  13. 13.
    Bouchard DR, Janssen I (2010) Dynapenic-obesity and physical function in older adults. J Gerontol A Biol Sci Med Sci 65:71–77. doi:10.1093/gerona/glp159 CrossRefPubMedGoogle Scholar
  14. 14.
    Stenholm S, Alley D, Bandinelli S, Griswold ME, Koskinen S, Rantanen T et al (2009) The effect of obesity combined with low muscle strength on decline in mobility in older persons: results from the InCHIANTI study. Int J Obes 33:635–644. doi:10.1038/ijo.2009.62 CrossRefGoogle Scholar
  15. 15.
    da Silva Alexandre T, de Oliveira Duarte YA, Ferreira Santos JL, Wong R, Lebrao ML (2014) Sarcopenia according to the European Working Group on Sarcopenia in Older People (EWGSOP) versus dynapenia as a risk factor for mortality in the elderly. J Nutr Health Aging 18:751–756. doi:10.1007/s12603-014-0450-3 CrossRefGoogle Scholar
  16. 16.
    Batsis JA, Barre LK, Mackenzie TA, Pratt SI, Lopez-Jimenez F, Bartels SJ (2013) Variation in the prevalence of sarcopenia and sarcopenic obesity in older adults associated with different research definitions: dual-energy X-ray absorptiometry data from the National Health and Nutrition Examination Survey 1999–2004. J Am Geriatr Soc 61:974–980. doi:10.1111/jgs.12260 CrossRefPubMedGoogle Scholar
  17. 17.
    Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F et al (2010) Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 39:412–423. doi:10.1093/ageing/afq034 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bauer JM, Sieber CC (2008) Sarcopenia and frailty: a clinician’s controversial point of view. Exp Gerontol 43:674–678. doi:10.1016/j.exger.2008.03.007 CrossRefPubMedGoogle Scholar
  19. 19.
    Abellan van Kan G, Cameron Chumlea W, Gillette-Guyonet S, Houles M, Dupuy C, Rolland Y et al (2011) Clinical trials on sarcopenia: methodological issues regarding phase 3 trials. Clin Geriatr Med 27:471–482. doi:10.1016/j.cger.2011.03.010 CrossRefPubMedGoogle Scholar
  20. 20.
    Cesari M, Landi F, Vellas B, Bernabei R, Marzetti E (2014) Sarcopenia and physical frailty: two sides of the same coin. Front Aging Neurosci 6:192. doi:10.3389/fnagi.2014.00192 PubMedPubMedCentralGoogle Scholar
  21. 21.
    Mitchell RJ, Lord SR, Harvey LA, Close JC (2014) Associations between obesity and overweight and fall risk, health status and quality of life in older people. Aust N Z J Public Health 38:13–18. doi:10.1111/1753-6405.12152 CrossRefPubMedGoogle Scholar
  22. 22.
    Sachdev PS, Brodaty H, Reppermund S, Kochan NA, Trollor JN, Draper B et al (2010) The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70–90 years. Int Psychogeriatr 22:1248–1264. doi:10.1017/S1041610210001067 CrossRefPubMedGoogle Scholar
  23. 23.
    Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198CrossRefPubMedGoogle Scholar
  24. 24.
    Hsu FC, Lenchik L, Nicklas BJ, Lohman K, Register TC, Mychaleckyj J et al (2005) Heritability of body composition measured by DXA in the diabetes heart study. Obes Res 13:312–319. doi:10.1038/oby.2005.42 CrossRefPubMedGoogle Scholar
  25. 25.
    Snijder MB, Visser M, Dekker JM, Seidell JC, Fuerst T, Tylavsky F et al (2002) The prediction of visceral fat by dual-energy X-ray absorptiometry in the elderly: a comparison with computed tomography and anthropometry. Int J Obes Relat Metab Disord 26:984–993. doi:10.1038/sj.ijo.0801968 CrossRefPubMedGoogle Scholar
  26. 26.
    Levine ME, Crimmins EM (2012) The impact of insulin resistance and inflammation on the association between sarcopenic obesity and physical functioning. Obesity 20:2101–2106. doi:10.1038/oby.2012.20 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Newman AKV, Visser M, Simonsick E, Goodpaster B, Nevitt M, Kritcheysky S, Tylavsky F, Rubin S, Harris T (2003) Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc 51:1602–1609CrossRefPubMedGoogle Scholar
  28. 28.
    Lord SR, Menz HB, Tiedemann A (2003) A physiological profile approach to falls risk assessment and prevention. Phys Ther 83:237–252PubMedGoogle Scholar
  29. 29.
    Lord SR, Ward JA, Williams P, Anstey KJ (1994) Physiological factors associated with falls in older community-dwelling women. J Am Geriatr Soc 42:1110–1117CrossRefPubMedGoogle Scholar
  30. 30.
    Lord SR, Ward JA, Williams P (1996) Exercise effect on dynamic stability in older women: a randomized controlled trial. Arch Phys Med Rehabil 77:232–236CrossRefPubMedGoogle Scholar
  31. 31.
    Lord SR, Fitzpatrick RC (2001) Choice stepping reaction time: a composite measure of falls risk in older people. J Gerontol A Biol Sci Med Sci 56:M627–M632CrossRefPubMedGoogle Scholar
  32. 32.
    Schoene D, Wu SM, Mikolaizak AS, Menant JC, Smith ST, Delbaere K et al (2013) Discriminative ability and predictive validity of the timed up and go test in identifying older people who fall: systematic review and meta-analysis. J Am Geriatr Soc 61:202–208. doi:10.1111/jgs.12106 CrossRefPubMedGoogle Scholar
  33. 33.
    Ustun TB, Chatterji S, Kostanjsek N, Rehm J, Kennedy C, Epping-Jordan J et al (2010) Developing the World Health Organization Disability Assessment Schedule 2.0. Bull World Health Organ 88:815–823. doi:10.2471/blt.09.067231 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Lamb SE, Jorstad-Stein EC, Hauer K, Becker C, Prevention of Falls Network E, Outcomes Consensus G (2005) Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc 53:1618–1622. doi:10.1111/j.1532-5415.2005.53455.x CrossRefPubMedGoogle Scholar
  35. 35.
    Manty M, Heinonen A, Viljanen A, Pajala S, Koskenvuo M, Kaprio J et al (2009) Outdoor and indoor falls as predictors of mobility limitation in older women. Age Ageing 38:757–761. doi:10.1093/ageing/afp178 CrossRefPubMedGoogle Scholar
  36. 36.
    Clark BC, Manini TM (2008) Sarcopenia =/= dynapenia. J Gerontol A Biol Sci Med Sci 63:829–834CrossRefPubMedGoogle Scholar
  37. 37.
    Hughes VA, Frontera WR, Wood M, Evans WJ, Dallal GE, Roubenoff R et al (2001) Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci 56:B209–B217CrossRefPubMedGoogle Scholar
  38. 38.
    Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV et al (2006) The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci 61:1059–1064CrossRefPubMedGoogle Scholar
  39. 39.
    Berger MJ, Doherty TJ (2010) Sarcopenia: prevalence, mechanisms, and functional consequences. Interdiscip Top Gerontol 37:94–114. doi:10.1159/000319997 CrossRefPubMedGoogle Scholar
  40. 40.
    Marcus RL, Addison O, Dibble LE, Foreman KB, Morrell G, Lastayo P (2012) Intramuscular adipose tissue, sarcopenia, and mobility function in older individuals. J Aging Res 2012:629637. doi:10.1155/2012/629637 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Inacio M, Ryan A, Bair W-N, Prettyman M, Beamer B, Rogers M (2014) Gluteal muscle composition differentiates fallers from non-fallers in community dwelling older adults. BMC Geriatr 14:37CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Corrigan D, Bohannon RW (2001) Relationship between knee extension force and stand-up performance in community-dwelling elderly women. Arch Phys Med Rehabil 82:1666–1672. doi:10.1053/apmr.2001.26811 CrossRefPubMedGoogle Scholar
  43. 43.
    Carty CP, Barrett RS, Cronin NJ, Lichtwark GA, Mills PM (2012) Lower limb muscle weakness predicts use of a multiple-versus single-step strategy to recover from forward loss of balance in older adults. J Gerontol A Biol Sci Med Sci 67:1246–1252. doi:10.1093/gerona/gls149 CrossRefPubMedGoogle Scholar
  44. 44.
    Tiedemann A, Sherrington C, Lord SR (2005) Physiological and psychological predictors of walking speed in older community-dwelling people. Gerontology 51:390–395. doi:10.1159/000088703 CrossRefPubMedGoogle Scholar
  45. 45.
    Wennie Huang WN, Perera S, VanSwearingen J, Studenski S (2010) Performance measures predict onset of activity of daily living difficulty in community-dwelling older adults. J Am Geriatr Soc 58:844–852. doi:10.1111/j.1532-5415.2010.02820.x CrossRefPubMedGoogle Scholar
  46. 46.
    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J et al (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56:M146–M157. doi:10.1093/gerona/56.3.M146 CrossRefPubMedGoogle Scholar
  47. 47.
    Cawthon PM, Fox KM, Gandra SR, Delmonico MJ, Chiou CF, Anthony MS et al (2009) Do muscle mass, muscle density, strength, and physical function similarly influence risk of hospitalization in older adults? J Am Geriatr Soc 57:1411–1419. doi:10.1111/j.1532-5415.2009.02366.x CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Lord SR, Sambrook PN, Gilbert C, Kelly PJ, Nguyen T, Webster IW et al (1994) Postural stability, falls and fractures in the elderly: results from the Dubbo Osteoporosis Epidemiology Study. Med J Aust 160(684–685):688–691Google Scholar
  49. 49.
    Bath PA, Morgan K (1999) Differential risk factor profiles for indoor and outdoor falls in older people living at home in Nottingham, UK. Eur J Epidemiol 15:65–73CrossRefPubMedGoogle Scholar

Copyright information

© International Osteoporosis Foundation and National Osteoporosis Foundation 2016

Authors and Affiliations

  • J. C. Menant
    • 1
    • 2
  • F. Weber
    • 1
  • J. Lo
    • 1
  • D. L. Sturnieks
    • 1
  • J. C. Close
    • 1
    • 3
  • P. S. Sachdev
    • 4
    • 5
  • H. Brodaty
    • 6
    • 7
  • S. R. Lord
    • 1
    • 2
  1. 1.Neuroscience Research AustraliaUniversity of New South WalesSydneyAustralia
  2. 2.School of Public Health and Community MedicineUniversity of New South WalesSydneyAustralia
  3. 3.Prince of Wales Clinical SchoolSydneyAustralia
  4. 4.Brain and Aging Research Program, School of Psychiatry, Faculty of MedicineUniversity of New South WalesSydneyAustralia
  5. 5.Neuropsychiatric Institute, Prince of Wales HospitalSydneyAustralia
  6. 6.Dementia Collaborative Research Centre—Assessment and Better CareUniversity of New South WalesRandwickAustralia
  7. 7.Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia

Personalised recommendations