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Official Journal of ICAPA
www.JAPA-Journal.com
SCHOLARLY REVIEW
441
The authors are with the Dept. of Kinesiology, University of Georgia,
Athens, GA. Address author correspondence to Anne Brady at aobrady@
uncg.edu
Journal of Aging and Physical Activity, 2014, 22, 441-452
http://dx.doi.org/10.1123/JAPA.2013-0009
© 2014 Human Kinetics, Inc.
Body Composition, Muscle Capacity, and Physical Function
in Older Adults: An Integrated Conceptual Model
Anne O. Brady, Chad R. Straight, and Ellen M. Evans
The aging process leads to adverse changes in body composition (increases in fat mass and decreases in skeletal muscle mass),
declines in physical function (PF), and ultimately increased risk for disability and loss of independence. Specic components
of body composition or muscle capacity (strength and power) may be useful in predicting PF; however, ndings have been
mixed regarding the most salient predictor of PF. The development of a conceptual model potentially aids in understanding the
interrelated factors contributing to PF with the factors of interest being physical activity, body composition, and muscle capac-
ity. This article also highlights sex differences in these domains. Finally, factors known to affect PF, such as sleep, depression,
fatigue, and self-efcacy, are discussed. Development of a comprehensive conceptual model is needed to better characterize
the most salient factors contributing to PF and to subsequently inform the development of interventions to reduce physical dis-
ability in older adults.
Keywords: interdisciplinary, physical activity, muscle quality
The number of individuals older than age 65 is rapidly increas-
ing across the world as well as in the United States. It is estimated
that by 2050, there will be 88.5 million older adults in the United
States, meaning one in ve individuals will be older than age 65
(Prole America: Facts for Features, 2011). Furthermore, during this
same time period, life expectancy in the United States is projected
to increase from 76.0 years to 82.6 years (Day, 1996). When com-
paring the sexes, older women continue to outnumber and outlive
older men (Werner, 2011).
It is well established that with increasing age, individuals are
more likely to experience functional declines, mobility limitations,
and physical disability (Homes, Powell-Grinder, Lethbridge-Cejku,
& Heyman, 2009). Therefore, with a large majority of baby boom-
ers (individuals born between 1946 and 1964) reaching old age in
combination with advanced life expectancy, the number of individu-
als with physical limitations will likely reach unprecedented levels.
According to the Centers for Disease Control and Prevention, physi-
cal limitations can be dened as difculty performing any of the
following activities: walking 0.25 mi; walking up 10 steps without
resting; standing or being on your feet for about 2 hours; sitting for
about 2 hours; stooping, bending, or kneeling; reaching up over your
head; using your ngers to grasp or handle small objects; lifting or
carrying something as heavy as 10 lb (4.5 kg; Homes et al., 2009).
As indicated by the Centers for Disease Control and Prevention’s
denition and the examples of functional tasks, physical disability
is largely determined by the lower body. Current estimates indicate
that 23% of individuals ages 60–69 years report one or more physi-
cal limitations, and the presence of physical limitations increases
with age (Homes et al., 2009). In addition, across all age groups,
women are more likely than men to have one or more physical
limitations (Homes et al., 2009). Therefore, as women outnumber
and outlive their male counterparts, sex demographics will likely
contribute to increased societal rates of physical disability. For these
reasons, it is critical to understand the factors that affect functional
limitations of the older adult population, because they are associ-
ated with increased rates of nursing home admissions and mortality
(Guralnik et al., 1994).
The increased number of older adults in combination with
disability is of great concern from a public health perspective. In
addition to functional declines and physical disability, older adults
may also experience decreased health-related quality of life. A
recently published article cited age, medical care costs, leisure time
physical activity, and smoking as factors that were most strongly
associated with both physical and mental health in older adults
(Thompson, Zack, Krahn, Andresen, & Barile, 2012). Furthermore,
it was estimated that about 27%, or $400 billion, of all U.S. adult
health care expenditures in 2006 were due to disability, inclusive
of physical, mental, or emotional types (Anderson, Armour, Fin-
kelstein, & Wiener, 2010).
Moreover, another major health concern for many older adults
is the risk of falling. Older adults are at greater risk of falls because
of several contributing factors, including declines in muscle strength
and power, poor balance, and reduced reaction times. Approximately
one in three older adults falls each year, with each fall requiring
hospitalization with an average cost of approximately $26,500
(Davis et al., 2010). Therefore, an aging population with increased
physical disabilities and decreased health-related quality of life is
expected to have both public health and economic implications in
the United States.
Development of Conceptual Model
A large body of literature has supported the interrelationships among
various factors affecting physical function (PF) in older adults
(Villareal et al., 2011). Such factors include physical activity, body
composition (fat mass and skeletal muscle mass), muscle capacity
(leg strength and leg power), and muscle quality, an assessment
combining a measure of body composition and muscle capacity.
Sex also affects each of these factors and contributes to differences
in PF between men and women. For example, in comparison with
442 Brady, Straight, and Evans
older men, older women tend to have higher amounts of body fat
(Jankowski et al., 2008; Valentine, Misic, Rosengren, Woods, &
Evans, 2009), lower muscle quality (Reid et al., 2012), and poorer
PF (Ferrucci et al., 2000; Millán-Calenti et al., 2010; Murtagh &
Hubert, 2004; Valentine, Misic, et al., 2009). More specically,
between the ages of 70 and 90, the proportion of disabled women
increased from 22% to 81%, whereas the proportion of disabled
men only increased from 15% to 57% (Leveille, Penninx, Melzer,
Izmirlian, & Guralnik, 2000). A comprehensive understanding of PF
in older adults must also recognize additional contributing factors,
such as sleep, fatigue, depression, and self-efcacy. Even the most
recent review articles and meta-analyses have only analyzed the
relationship between a few of these factors and PF in older adults
(den Ouden, Schuurmans, Arts, & van der Schouw, 2011; Liu &
Latham, 2011; Schaap, Koster, & Visser, 2013), indicating a lack
of studies using a truly integrated approach. The individual and
synergistic effects of the primary variables (e.g., muscle strength,
body composition) must be considered in addition to the important
additional contributing factors (e.g., self-efcacy).
We use the conceptual model depicted in Figure 1 as a frame-
work to explore factors that inuence PF in older adults. Specic
areas of exploration include the relationships among age and
changes in (a) physical activity, (b) body composition, (c) muscle
capacity, and (d) PF. Because differences have been documented
between men and women in many of these areas, we also highlight
the impact of sex on these domains. In addition, the inuence of
other contributing factors on PF is also discussed. The development
and use of this conceptual model will aid in improving interven-
tion strategies by identifying target areas that contribute to PF and
physical disability.
Physical Activity Changes With Age
Research has indicated that older adults who engage in greater
amounts of physical activity have more favorable body composition
(Chastin, Ferriolli, Stephens, Fearon, & Greig, 2012) and greater
muscle strength (Van Roie et al., 2010). However, it has also been
well documented that the amount of physical activity declines with
age (DiPietro, Williamson, Caspersen, & Eaker, 1993; Schoenborn
& Adams, 2010; Westerterp, 2000). Declines in physical activity
affect body composition and muscle capacity and are associated
with decrements in muscle strength and PF (Evans, 2010). These
changes contribute to declines in PF and increased disability.
More specically, research has indicated that older adults tend to
engage in fewer high-intensity activities as they age, and there is
an increase in sedentary behavior (DiPietro, 2001). This trend is
specically evident among older women (DiPietro, 2001). The
age-related decline in physical activity is particularly concerning
because a recent prospective study reported that physical activity
is inversely related to all-cause mortality in older adults, with the
relationship being stronger in women than men across all levels of
physical activity (Brown et al., 2012).
Although the likelihood of physical limitations and disability
increases with age, multiple studies have demonstrated that exercise
is an effective intervention strategy for improving PF in older adults.
Figure 1 — Conceptual model: Factors affecting physical function in older adults. PA = physical activity.
Conceptual Model of Physical Function 443
In particular, the effects of resistance training interventions on PF
in older adults are robust (Hunter, McCarthy, & Bamman, 2004),
accounting for the inclusion of strength training in position state-
ments for physical activity and older adults (M. E. Nelson et al.,
2007). Several intervention trials have reported improvements in PF
after a resistance training program in relatively healthy older adults
(Avila, Gutierres, Sheehy, Lofgren, & Delmonico, 2010; Baker et
al., 2001; Capodaglio, Capodaglio Edda, Facioli, & Saibene, 2007;
Fiatarone et al., 1994; Henwood & Taaffe, 2005; Hruda, Hicks, &
McCartney, 2003; Miszko et al., 2003; K. R. Vincent et al., 2002)
as well as older adults with chronic health conditions (Brochu et
al., 2002; Kongsgaard, Backer, Jorgensen, Kjaer, & Beyer, 2004;
Ouellette et al., 2004; Weiss, Suzuki, Bean, & Fielding, 2000; Yang,
Wang, Lin, Chu, & Chan, 2006). Moreover, community-based
resistance training interventions that use a lighter training intensity
but have greater translational value have also been shown to be
effective at improving functional outcomes in community-dwelling
older adults (Straight, Lofgren, & Delmonico, 2012). In addition,
aerobic training is often a cornerstone of an exercise program and
has also been found to be benecial at improving PF in older adults
(Davidson et al., 2009; Ettinger et al., 1997). Furthermore, stud-
ies have shown that weight loss interventions have the capacity to
improve PF in obese older adults (Jensen, Roy, Buchanan, & Berg,
2004; Miller et al., 2006). However, recent evidence has suggested
that a combination of exercise and weight loss may result in greater
improvements in PF than either intervention strategy alone (Villar-
eal, Banks, Sinacore, Siener, & Klein, 2006; Villareal et al., 2011).
Thus, although exercise and weight loss interventions both confer
functional benets, additional research is necessary to ascertain the
optimal treatment strategy for improving PF in older adults.
Body Composition Changes With Age
The aging process is accompanied by changes in body composition,
specically an increase in adiposity and a decrease in muscle mass
(Goodpaster et al., 2006). Notably, obesity has been reported as
the leading cause of disability among older adults (Chen & Guo,
2008; Villareal, Apovian, Kushner, & Klein, 2005). It is estimated
that approximately 37% of men and 42% of women older than age
60 are obese, as dened by a body mass index of 30 kg/m
2
or more
(Ogden, Carroll, Kit, & Flegal, 2012). This is concerning because
a recent meta-analysis reported that older adults with a body mass
index of 30 kg/m
2
or more are 60% more likely to experience
functional declines than their normal-weight counterparts (Schaap
et al., 2013). Adiposity, specically central adiposity, is associated
with an increased risk for chronic diseases, such as cardiovascular
disease, diabetes, and cancer (Chang, Beason, Hunleth, & Colditz,
2012), which indirectly contributes to functional declines (Hung,
Ross, Boockvar, & Siu, 2012; Kalyani, Saudek, Brancati, &
Selvin, 2010). Furthermore, a recent review article highlighted the
signicant association between waist circumference, often used as
a surrogate measure of central adiposity, and functional declines
(Schaap et al., 2013). Less is known about the role of lower body
adiposity on PF; however, the ratio of trunk fat to lower limb fat is
not signicantly associated with disability in older adults (Foster et
al., 2010); rather, it is relative adiposity (i.e., body fat percentage)
that contributes to disability (Alley, Ferrucci, Barbagallo, Studenski,
& Harris, 2008; Villareal et al., 2005). When grouped by fat index
(total body fat normalized for height), PF is signicantly lower in a
high fat index group than in a low fat index group (Jankowski et al.,
2008). Furthermore, in comparison with older men, older women
tend to have greater amounts of fat mass, which has greater impli-
cations for PF, thus placing them at an increased risk for physical
disability (Jankowski et al., 2008; Valentine, Misic, et al., 2009).
In addition to increases in overall body fatness, thigh intermus-
cular adipose tissue increases with age (Buford et al., 2012; Del-
monico et al., 2009; Visser et al., 2005). In a recent cross-sectional
study, no differences in PF were found when comparing older adults
with high and low intermuscular adipose tissue (Buford et al., 2012).
In contrast, a longitudinal study by Visser et al. (2005) reported fat
inltration in the midthigh is an independent risk factor for mobil-
ity limitations. Thus, more research is warranted to determine the
impact of fat inltration within the muscle and its impact on PF.
Sarcopenia, meaning “poverty of esh,” is an age-associated
loss of skeletal muscle mass (Rosenberg, 1989). The underlying
mechanisms contributing to sarcopenia are multifactorial and
include altered endocrine function, increased inammation, mito-
chondrial dysfunction, inadequate nutrition, and cellular apoptosis
(Rolland et al., 2008). In community-dwelling older adults, sar-
copenia has been associated with reductions in muscle capacity
(Newman et al., 2003), which often results in physical functional
declines (Alley et al., 2008; Janssen, Heymseld, & Ross, 2002;
H. K. Vincent, Vincent, & Lamb, 2010) and physical disability
(Baumgartner et al., 1998; Janssen, Baumgartner, Ross, Rosenberg,
& Roubenoff, 2004). Older women tend to have lower amounts of
total skeletal muscle mass in comparison with older men (Jankowski
et al., 2008; Valentine, Misic, et al., 2009), again placing them at
greater risk for functional declines. However, the rate of skeletal
muscle mass decline should also be considered and is about 6%
per decade after age 50 (Lynch et al., 1999). With regard to the
impact of sex, a longitudinal analysis using the Health, Aging, and
Body Composition Study cohort found that the rate of decline in
leg muscle mass was similar across men and women (Goodpaster
et al., 2006).
Although both increases in adiposity and declines in skeletal
muscle mass independently contribute to functional declines, the
synergistic effects of these body composition changes further exac-
erbate the physical disability process. Individuals at greatest risk
for functional declines and subsequent disability are those with an
excessive amount of fat mass and inadequate skeletal muscle mass
(Schaap et al., 2013), a body composition disorder termed sarco-
penic obesity. Alarmingly, this somatotype represents a growing
proportion of the older adult population (Alley et al., 2008; Chen
& Guo, 2008; Jarosz & Bellar, 2009; Rolland et al., 2009; H. K.
Vincent et al., 2010). Among sarcopenic obese older adults com-
pared with normal-weight older adults, the relative risk for onset
of instrumental activities of daily living disability was 2.63 (95%
CI [1.19, 5.85]; Baumgartner et al., 2004). Compared with their
male counterparts, older women have greater amounts of fat mass
(Jankowski et al., 2008; Valentine, Misic, et al., 2009) and lower
amounts of muscle mass (Jankowski et al., 2008; Valentine, Misic,
et al., 2009), which may make them more predisposed to a sarco-
penic obesity phenotype. Furthermore, older women identied as
sarcopenic obese had a signicantly higher risk of difculty with
stair ascent and descent in comparison with sarcopenic, obese, and
normal-weight older women (Rolland et al., 2009). In contempo-
rary society, as physical activity levels decline, with subsequent
reductions in skeletal muscle mass and increases in obesity, body
composition will undoubtedly play a key role in the sex differences
in PF and disability rates in the future.
444 Brady, Straight, and Evans
Muscle Capacity Changes With Age
Muscle capacity measures, including muscle strength and muscle
power, more appropriately capture an individual’s functional abili-
ties because these assessments incorporate utilization of skeletal
muscle mass (Woods, Iuliano-Burns, King, Strauss, & Walker,
2011). Although muscle strength and power declines can be related
to changes in habitual physical activity, other changes in the mus-
culoskeletal system occur during the aging process, subsequently
affecting PF. Such alterations include signicant declines in neu-
romuscular function and performance (Doherty, Vandervoort, &
Brown, 1993; Doherty, 2001, 2003) and loss of skeletal muscle mass
(Newman et al., 2006). Our review is focused on changes occurring
in the major lower extremity muscle groups (gluteals, hamstrings,
and quadriceps) because one’s ability to complete activities of daily
living and functional tasks (i.e., rising from a chair, climbing stairs)
is largely determined by the lower body musculature.
Recent evidence has suggested that merely assessing body
composition or a cross-sectional area of the muscle may be an
inadequate measure to ascertain information regarding PF in older
adults. Indeed, several studies have found a disassociation between
loss of muscle mass and loss of muscle strength (Barbat-Artigas,
Dupontgand, Fex, Karelis, & Aubertin-Leheudre, 2011; Newman
et al., 2006; Visser et al., 2000). For this reason, the term dyna-
penia has been suggested to describe the age-related loss of muscle
strength to differentiate between declines in mass (sarcopenia) and
strength (Clark & Manini, 2008). In a 5-year longitudinal study
by Delmonico et al. (2009), the rate of strength loss, measured as
maximum isokinetic knee extensor torque, was about 3–4 times
greater than the loss of muscle size, measured as a cross-sectional
area of the midthigh via computed tomography. Related to this, a
recent meta-analysis reported that muscle strength, but not muscle
mass, is associated with functional declines, with odds ratios of 1.86
(95% CI [1.32, 2.64]) and 1.19 (95% CI [0.98, 1.45]), respectively
(Schaap et al., 2013).
Muscle Strength and Muscle Power
Comparing across ages, individuals in the oldest age groups have
lower muscle strength, muscle power, and muscle quality than
their younger counterparts (Bouchard, Héroux, & Janssen, 2011;
Newman et al., 2003, 2006). Specically, using a cross-sectional
sample of men and women from NHANES grouped by age (55–64,
65–74, 75 and older), Bouchard et al. (2011) reported that maximal
quadriceps strength measured via isokinetic dynamometry declined
as age increased. In addition, across each age category, women had
lower maximal values than their age-matched male counterparts
(Bouchard et al., 2011). In the longitudinal Health ABC study, older
adults’ (ages 70–79) quadriceps muscle strength as measured by
isokinetic dynamometry declined at an annual rate of 3.6% and 2.8%
in men and women, respectively (Goodpaster et al., 2006). Similar
to sarcopenic obesity, dynapenic obesity (low muscle strength in
combination with excessive adiposity) also increases risk for dis-
ability in older adults (Bouchard & Janssen, 2010; Stenholm et
al., 2009). The drastic declines in lower body muscle strength per
year contribute to loss of mobility and ultimately decrements in PF.
Muscle power also decreases with age, and the decline occurs
at a more rapid rate than that of muscle strength (Barry & Carson,
2004). It is estimated that declines in muscle power are 10% greater
than losses in muscle strength in older adults (Metter, Conwit,
Tobin, & Fozard, 1997). Numerous factors contribute to the loss of
muscle power with age, including changes in ber type, motor unit
recruitment, neural factors, and intermuscular coordination (Barbat-
Artigas, Rolland, Zamboni, & Aubertin-Leheudre, 2012). A recent
review article suggested that using a measure of muscle power may
be a more complex but more appropriate index (Barbat-Artigas
et al., 2012) and may more accurately predict PF in older adults.
Additional studies have supported the theory that muscle power is
a more robust predictor of PF than muscle strength (Bean, Kiely,
LaRose, & Leveille, 2008). In older adults, muscle power is strongly
associated with gait speed (Cuoco et al., 2004), balance (Orr et al.,
2006), and functional status (Foldvari et al., 2000). Furthermore,
muscle power has been found to be more important to activities of
daily living than muscle strength (Bean et al., 2002; Foldvari et al.,
2000), because many activities (stair climbing, lifting one’s body
from a bed or chair, and carrying groceries) require greater use of
muscle power relative to muscle strength. Across all age groups,
men have greater muscle power than women (Bassey et al., 1992;
Caserotti, Aagaard, Simonsen, & Puggaard, 2001; Metter et al.,
1997; Reid et al., 2012). However, the rate of decline in muscle
power is greater in men (3%) than women (1.7%; Skelton, Greig,
Davies, & Young, 1994). Because the age-related decrease in muscle
capacity (strength and power) exceeds the loss of muscle mass
(Barbat-Artigas et al., 2012; Delmonico et al., 2009; Goodpaster
et al., 2006), investigators have determined that an accompanying
decline in muscle quality also occurs.
Muscle Quality
One approach to operationalizing muscle quality is dening it as
muscle capacity per kilogram of body weight or muscle size (cross-
sectional area or mass; Lynch et al., 1999; Metter et al., 1999; Tracy
et al., 1999). A variety of techniques exist to measure muscle capac-
ity and muscle size. The most commonly used laboratory measure
of muscle capacity is isokinetic dynamometry (Delmonico et al.,
2009; Goodpaster et al., 2006; Misic, Rosengren, Woods, & Evans,
2007; Newman et al., 2003). Fewer studies have implemented a
measure of muscle power to dene muscle quality (Reid, Naumova,
Carabello, Phillips, & Fielding, 2008; Straight, Brady, Schmidt, &
Evans, 2013). With regard to muscle size, most studies have used
dual-energy X-ray absorptiometry (DXA) scanning to quantify leg
muscle mass (Bouchard et al., 2011; Goodpaster et al., 2006; Misic
et al., 2007; Newman et al., 2003). We should note that using DXA
to determine skeletal muscle mass does not take into account inter-
muscular fat, which may affect muscle quality. Methods allowing
for intermuscular adipose tissue quantication include computed
tomography and MRI. Despite the measurement method used,
consistent ndings have supported a loss of muscle quality with age
(Delmonico et al., 2009; Goodpaster et al., 2006; Newman et al.,
2003). Comparing across sex, longitudinal studies have indicated
that women experience a loss of muscle quality at a rate of about
2% per year, and men showed a slightly faster decline of about
2.5% per year (Delmonico et al., 2009; Goodpaster et al., 2006).
Physical Function
Reductions in habitual physical activity, adverse changes in body
composition, and declines in muscle capacity are all important
factors contributing to declines in PF in older adults. In studies
comparing PF across sex, men have higher PF than their female
counterparts (Tseng et al., 2013; Valentine, Misic, et al., 2009; H.
K. Vincent et al., 2010). As stated previously, men tend to have
lower fat mass, greater muscle mass, and greater muscle strength
Conceptual Model of Physical Function 445
and power than women. Studies have indicated that the disparity in
body composition, particularly higher fat mass in women, signi-
cantly contributes to lower PF compared with men (Tseng et al.,
2013; Valentine, Misic, et al., 2009).
A contemporary research theme regarding older adults and PF
is determining which component of body composition or muscle
capacity most strongly contributes to PF. Such research endeavors
could inform the investigation of effective intervention strategies
to prevent PF decline in older adults. Some evidence has suggested
that adiposity is a stronger contributor to lower extremity PF than
muscle mass or sarcopenia (Bouchard et al., 2011; Jankowski et al.,
2008; Kidde, Marcus, Dibble, Smith, & Lastayo, 2009). Still other
studies have suggested that the amount of lean mass is the strongest
predictor of PF in older adults (Reid et al., 2008). In particular, Reid
et al. (2008) reported that increasing leg lean mass by 1 kg decreases
the odds of functional limitations by 53% in mobility-limited older
adults. However, as previously discussed, merely assessing body
composition without muscle capacity information may provide
insufcient information to predict PF.
An inconsistency remains in the literature regarding which
muscle capacity measure best predicts PF in older adults. Cross-
sectional data from NHANES, including 1,280 older adults strati-
ed by age, indicated that leg muscle mass (assessed via DXA),
leg extension strength (assessed via isokinetic dynamometry), and
muscle quality (dened as strength per unit muscle mass) decreased
with age and, subsequently, PF also signicantly decreased with
age (Bouchard et al., 2011). Furthermore, leg strength and fat mass
were independently associated with PF, regardless of age and sex.
Muscle mass and muscle quality were not found to be independently
associated with PF in this sample (Bouchard et al., 2011). In con-
trast, numerous other studies have reported that muscle power is
strongly related to PF and is a signicant predictor in older adults
(Bean et al., 2002, 2003; Berger & Doherty, 2010; Sayers, Guralnik,
Thombs, & Fielding, 2005).
More recently, studies have investigated the association
between muscle quality and PF. Misic et al. (2009) reported that, in
a sample of community-dwelling older adults, muscle quality (leg
muscle strength via isokinetic dynamometry divided by mineral-free
lean mass of the leg as measured by DXA) was the most important
predictor of lower extremity PF, explaining 2–42% of the variance
(Misic, Valentine, Rosengren, Woods, & Evans, 2009). Likewise, a
recent study showed that an index of muscle quality incorporating
power is an independent predictor of lower extremity PF (6-min
walk, 8-ft up and go, 30-s chair stand) in community-dwelling older
women (Straight et al., 2013). Notably, a modest increase (10%)
in muscle quality predicted an improvement in lower extremity PF
of 4.4%. Collectively, these ndings support the contribution of
muscle quality as a salient contributor to PF in community-dwelling
older adults.
Which body composition or muscle capacity measure most
strongly contributes to and predicts PF in older adults remains
unclear. Findings vary depending on the type of assessments used
and PF status of participants. On the basis of the available evidence,
it appears that muscle power may be the strongest contributor to
PF in mobility-limited older adults (Bean et al., 2002; Sayers et al.,
2005), whereas adiposity may be the strongest contributor to PF in
higher functioning older adults (Bouchard et al., 2011; Jankowski
et al., 2008). However, no study has compared the relative contribu-
tions of body composition (fat mass and muscle mass) and muscle
capacity (strength and power) with PF in groups of low- and high-
function older adults.
Contributing Factors to Physical Function
Although our conceptual model focuses on the importance of physi-
cal activity, body composition, and muscle capacity measures for PF,
myriad additional factors may contribute to PF in older adults. These
factors include the presence of chronic health conditions (Boult,
Kane, Louis, Boult, & McCaffrey, 1994; Dunlop, Manheim, Sohn,
Liu, & Chang, 2002; Groll, To, Bombardier, & Wright, 2005; Stuck
et al., 1999; Wang, van Belle, Kukull, & Larson, 2002), markers
of inammation (Brinkley et al., 2009; Cesari et al., 2004; Cohen,
Pieper, Harris, Rao, & Currie, 1997; Hsu et al., 2009; Penninx et
al., 2004; Taaffe, Harris, Ferrucci, Rowe, & Seeman, 2000; Tiainen,
Hurme, Hervonen, Luukkaala, & Jylha, 2010; Verghese et al., 2011),
cognition (Atkinson et al., 2007; Auyeung et al., 2008; Burton,
Strauss, Bunce, Hunter, & Hultsch, 2009; Carlson et al., 1999;
Pereira, Yassuda, Oliveira, & Forlenza, 2008; Rosano et al., 2005;
Soumaré, Tavernier, Alpérovitch, Tzourio, & Elbaz, 2009; Stuck et
al., 1999; Tuokko, Morris, & Ebert, 2005; Wang et al., 2002), smok-
ing (H. D. Nelson, Nevitt, Scott, Stone, & Cummings, 1994; Stuck
et al., 1999; van den Borst et al., 2011; Wang et al., 2002), social
support (Hays, Saunders, Flint, Kaplan, & Blazer, 1997; Kaplan,
Strawbridge, Camacho, & Cohen, 1993; Unger, McAvay, Bruce,
Berkman, & Seeman, 1999), sleep (Dam et al., 2008; Goldman et
al., 2007), fatigue (Moreh, Jacobs, & Stessman, 2010; Vestergaard
et al., 2009), depression (Hybels, Pieper, & Blazer, 2009; Penninx,
Leveille, Ferrucci, van Eijk, & Guralnik, 1999; Stuck et al., 1999;
Wang et al., 2002), and self-efcacy (McAuley et al., 2006, 2007;
Rejeski, Ettinger, Martin, & Morgan, 1998). Although a compre-
hensive analysis of these factors is beyond the scope of this article,
the impact of sleep, fatigue, depression, and self-efcacy on PF is
of considerable interest and warrants particular attention.
A growing number of published studies have reported a rela-
tionship between sleep-related problems and compromised PF in
older adults. Early studies reported a relationship between sleep
complaints and physical disabilities (Foley et al., 1995), as well
as between daytime sleepiness and limitation of activities of daily
living in older adults (Whitney et al., 1998). Likewise, sleep dis-
turbance has been identied as a signicant predictor of perceived
limitations in usual role activities among community-dwelling older
men and women (Kutner, Schechtman, Ory, & Baker, 1994). More
recently, it has been shown that the risk of functional limitations is
greater among older women with poor sleep quality (Goldman et al.,
2007), and sleep-related problems are associated with poorer PF in
community-dwelling older men (Dam et al., 2008). Other research
has shown that both sleep duration and insomnia are associated
with decreased gait speed and mobility limitation in older men and
women (Stenholm et al., 2010). Thus, sleep-related problems appear
to contribute to reduced PF, and interventions that improve sleep
quality in older adults may confer functional benets.
In addition to its association with various dimensions of PF,
sleep likely contributes to the presence of fatigue in older adults. In
this context, subjective fatigue is dened as a general sensation of
tiredness or having difculty initiating physical or mental activity
over several days to weeks (Lou, 2009). The prevalence of fatigue
increases with age (Moreh et al., 2010) and is higher among older
women than older men (Vestergaard et al., 2009). Fatigue has also
been reported as a primary cause of disability by community-dwell-
ing older women (Leveille, Fried, & Guralnik, 2002). Although the
relationship between fatigue and PF has not been well characterized,
emerging evidence has suggested that fatigue may increase risk for
adverse physical functional outcomes in older adults. For instance,
446 Brady, Straight, and Evans
a recent cross-sectional study found that fatigue was associated with
total score on the short physical performance battery and walking
speed, as well as mobility and instrumental activities of daily living
disability (Vestergaard et al., 2009). These ndings corroborate
previous research that found tiredness in daily activities was asso-
ciated with onset of physical disability in nondisabled older adults
(Avlund, Rantanen, & Schroll, 2006). Recently, fatigue has been
associated with self-rated health, functional status, and physical
activity level in community-dwelling older adults (Moreh et al.,
2010). Notably, fatigue has been identied as the most common
reason for restricted activity among community-dwelling older
adults (Gill, Desai, Gahbauer, Holford, & Williams, 2001). Thus,
the efcacy of interventions designed to ameliorate fatigue in older
adults should be further examined because they may have a positive
impact on PF in older adults.
Another psychosocial factor likely contributing to PF in
older adults is the presence of depression. Previous longitudinal
research has shown that depressive symptoms are associated with
an increased risk of disability in activities of daily living (Bruce,
Seeman, Merrill, & Blazer, 1994). Likewise, older adults with
depressive symptoms have worse PF than those without depressive
symptoms (Callahan et al., 1998; Wells et al., 1989). Similarly,
depressive symptoms have been identied as a signicant predictor
of decline in PF in community-dwelling older adults (Hays et al.,
1997). Other research has shown that the risk of incident disability
in activities of daily living and mobility is 39% and 45% greater,
respectively, in depressed older adults relative to nondepressed
adults (Penninx et al., 1999). Despite the adverse consequences
of depression, studies have shown that both pharmacologic and
behavioral interventions to reduce depression can have a positive
impact on PF in older adults (Callahan et al., 2005; Lin et al., 2003;
Penninx et al., 2002).
Last, a large body of literature has supported the contribution
of self-efcacy to PF in older adults. Self-efcacy has been asso-
ciated with gait speed (Rosengren, McAuley, & Mihalko, 1998),
fear of falling (Fuzhong et al., 2002), and functional limitations
(McAuley et al., 2006) in older adults. Similarly, self-efcacy has
been identied as an independent predictor of declines in perceived
functional abilities among older men and women (Seeman, Unger,
McAvay, & Mendes de Leon, 1999). More important, self-efcacy
has also been identied as a determinant of exercise participation
(McAuley, 1993; McAuley & Blissmer, 2000) and mediates the
relationship between physical activity and functional limitations
in older women (McAuley et al., 2007). Likewise, self-efcacy
mediates the effects of exercise intervention on stair ascent in older
adults with knee osteoarthritis (Rejeski et al., 1998) and has been
implicated in improved PF after tai chi in older women (Li et al.,
2001). Thus, there is strong evidence that self-efcacy is a salient
determinant of PF and should be targeted in interventions designed
to improve PF in older adults.
As stated previously, a multitude of physical and psychosocial
factors likely contribute to PF in older adults, and our conceptual
model attempts to delineate some of the most salient determinants.
In addition, the interrelationships between these factors (i.e., sleep,
fatigue, depression, self-efcacy) are complex and have not been
fully elucidated. For instance, in healthy older adults, inammation
has been related to fatigue (Valentine, Woods, McAuley, Dantzer,
& Evans, 2011); however, fatigue has also been associated with
depression (Bixler et al., 2005; Valentine, McAuley, et al., 2009).
Moreover, a recent review indicated that sleep complaints are
observed in 50–90% of patients with diagnosed depression (Tsuno,
Besset, & Ritchie, 2005). The interaction between these factors and
the subsequent consequences for PF have not been well character-
ized and should be further explored.
Conclusion
In conclusion, the growing number of older adults combined with
the obesity epidemic is expected to result in unprecedented levels
of functional limitations and physical disability in older Americans.
Although much research has focused on the relationships among
body composition, muscle capacity, and PF, which component most
strongly contributes to and predicts PF in older adults has yet to be
determined. Our proposed conceptual model provides a framework
to aid in a comprehensive analysis of PF encompassing all of the
aforementioned variables. More important, there is a need to develop
an interdisciplinary approach to studying PF in older adults inclusive
of behavioral factors (physical activity), physiological factors (body
composition, muscle capacity), and additional contributing factors
(sleep, fatigue, depression, self-efcacy). A more comprehensive
approach to studying PF may help elucidate specic strategies
and intervention techniques to improve PF and delay disability in
older adults.
Acknowledgment
The authors have no nancial or other conict of interest to report.
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