Body Composition, Muscle Capacity, and Physical Function in Older ...

Journal of Aging and Physical Activity, 2014, 22, 441-452 http://dx.doi.org/10.1123/JAPA.2013-0009 © 2014 Human Kinetics, Inc.

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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. Specific components of body composition or muscle capacity (strength and power) may be useful in predicting PF; however, findings 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 capacity. This article also highlights sex differences in these domains. Finally, factors known to affect PF, such as sleep, depression, fatigue, and self-efficacy, 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 disability in older adults. Keywords: interdisciplinary, physical activity, muscle quality The number of individuals older than age 65 is rapidly increasing 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 five individuals will be older than age 65 (Profile 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 comparing 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 boomers (individuals born between 1946 and 1964) reaching old age in combination with advanced life expectancy, the number of individuals with physical limitations will likely reach unprecedented levels. According to the Centers for Disease Control and Prevention, physical limitations can be defined as difficulty 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 fingers 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 definition 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 physical 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 The authors are with the Dept. of Kinesiology, University of Georgia, Athens, GA. Address author correspondence to Anne Brady at aobrady@ uncg.edu

reasons, it is critical to understand the factors that affect functional limitations of the older adult population, because they are associated 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, Finkelstein, & 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 441

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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 specifically, 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-efficacy. 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-efficacy). We use the conceptual model depicted in Figure 1 as a framework to explore factors that influence PF in older adults. Specific 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 influence 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 specifically, 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 specifically 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.

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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 statements 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 beneficial at improving PF in older adults (Davidson et al., 2009; Ettinger et al., 1997). Furthermore, studies 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 (Villareal, Banks, Sinacore, Siener, & Klein, 2006; Villareal et al., 2011). Thus, although exercise and weight loss interventions both confer functional benefits, 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, specifically 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 defined by a body mass index of 30 kg/m2 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/m2 or more are 60% more likely to experience functional declines than their normal-weight counterparts (Schaap et al., 2013). Adiposity, specifically 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 significant 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 significantly 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 significantly 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 implications 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 intermuscular adipose tissue increases with age (Buford et al., 2012; Delmonico 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 infiltration in the midthigh is an independent risk factor for mobility limitations. Thus, more research is warranted to determine the impact of fat infiltration within the muscle and its impact on PF. Sarcopenia, meaning “poverty of flesh,” 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 inflammation, mitochondrial dysfunction, inadequate nutrition, and cellular apoptosis (Rolland et al., 2008). In community-dwelling older adults, sarcopenia has been associated with reductions in muscle capacity (Newman et al., 2003), which often results in physical functional declines (Alley et al., 2008; Janssen, Heymsfield, & 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 exacerbate 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 sarcopenic 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 compared 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 sarcopenic obesity phenotype. Furthermore, older women identified as sarcopenic obese had a significantly higher risk of difficulty with stair ascent and descent in comparison with sarcopenic, obese, and normal-weight older women (Rolland et al., 2009). In contemporary 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.

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Muscle Capacity Changes With Age Muscle capacity measures, including muscle strength and muscle power, more appropriately capture an individual’s functional abilities 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 musculoskeletal system occur during the aging process, subsequently affecting PF. Such alterations include significant declines in neuromuscular 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 dynapenia 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). Specifically, 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 disability 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 fiber type, motor unit

recruitment, neural factors, and intermuscular coordination (BarbatArtigas, 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 defining it as muscle capacity per kilogram of body weight or muscle size (crosssectional area or mass; Lynch et al., 1999; Metter et al., 1999; Tracy et al., 1999). A variety of techniques exist to measure muscle capacity 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 define 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 intermuscular fat, which may affect muscle quality. Methods allowing for intermuscular adipose tissue quantification include computed tomography and MRI. Despite the measurement method used, consistent findings 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

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and power than women. Studies have indicated that the disparity in body composition, particularly higher fat mass in women, significantly 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 insufficient information to predict PF. An inconsistency remains in the literature regarding which muscle capacity measure best predicts PF in older adults. Crosssectional data from NHANES, including 1,280 older adults stratified by age, indicated that leg muscle mass (assessed via DXA), leg extension strength (assessed via isokinetic dynamometry), and muscle quality (defined as strength per unit muscle mass) decreased with age and, subsequently, PF also significantly 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 contrast, numerous other studies have reported that muscle power is strongly related to PF and is a significant 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 findings 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 contributions of body composition (fat mass and muscle mass) and muscle capacity (strength and power) with PF in groups of low- and highfunction older adults.

Contributing Factors to Physical Function Although our conceptual model focuses on the importance of physical 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 inflammation (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), smoking (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-efficacy (McAuley et al., 2006, 2007; Rejeski, Ettinger, Martin, & Morgan, 1998). Although a comprehensive analysis of these factors is beyond the scope of this article, the impact of sleep, fatigue, depression, and self-efficacy on PF is of considerable interest and warrants particular attention. A growing number of published studies have reported a relationship 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 disturbance has been identified as a significant 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 benefits. 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 defined as a general sensation of tiredness or having difficulty 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-dwelling 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,

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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 findings corroborate previous research that found tiredness in daily activities was associated 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 identified as the most common reason for restricted activity among community-dwelling older adults (Gill, Desai, Gahbauer, Holford, & Williams, 2001). Thus, the efficacy 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 identified as a significant 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-efficacy to PF in older adults. Self-efficacy has been associated 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-efficacy has been identified 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-efficacy has also been identified 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-efficacy 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-efficacy 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-efficacy) are complex and have not been fully elucidated. For instance, in healthy older adults, inflammation 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 characterized 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-efficacy). A more comprehensive approach to studying PF may help elucidate specific strategies and intervention techniques to improve PF and delay disability in older adults. Acknowledgment The authors have no financial or other conflict of interest to report.

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