A. History of Recognition of Physical Inactivity

Physical inactivity was recognized at least 2,500 yr ago. Physical inactivity has been based on health for millennia. In ∼600 BC, Susruta believed that regular moderate exercise offered resistance to disease(s) and “against physical decay” (497). In ∼400 BC, Hippocrates wrote “eating alone will not keep a man well; he must also take exercise, for food and exercise work together . . . to produce health” (35). Tipton (496) and Myers et al. (348) discuss a more complete history from Hippocrates to 50 yr ago. Paffenbarger, Blair, and Lee (376) recounted how Morris et al. (342) published in 1953 that London bus drivers, whose occupation was continuous sitting, had greater incidence of coronary heart disease twice that of physically active conductors in London double-decker buses. Taken together, physical inactivity has been historically defined based on its effect on health. To define “inactivity” in this review, we will base the definition on its impact on health.

Using the first U.S. Physical Activity Guidelines published in 2008 (511), we have set arbitrary time durations for physical activity based on public health criteria. The definition is <60 min/day of physical activity for ages of 17 yr old and under and <150 min of weekly physical activity for ages of 18 yr and older; these are minimum requirements for health (Table 1).

The U.S. definition of physical inactivity is similar to the WHO’s (541) definition. WHO divides physical inactivity into two classifications. Level 1 of physical inactivity (inactive) is defined as “doing no or very little physical activity at work, at home, for transport or in discretionary time.” Level 2 of physical inactivity (insufficiently active) is defined by WHO as “doing some physical activity, but less than 150 minutes of moderate-intensity physical activity or 60 minutes of vigorous-intensity physical activity a week accumulated across work, home, transport or discretionary domains (141). The current review considers the two WHO categories of physical inactivity together as a part of a continuum, as illustrated in FIGURE 2, providing a continuum of theme 4.

B. Physical Inactivity Has Increased in the Last Century

Societies today that do not employ power-driven machines and motorized transportation can provide estimates of what daily step count might have been centuries ago, allowing an educated guess as to the increase in physical inactivity seen today. Bassett et al. (24) provided one such estimate in The Old Order Amish in Canada who today refrain from using automobiles, electrical appliances, and other modern conveniences, and their occupation is labor-intensive farming. Men and women averaged ~18,000 and ~14,000 steps/day, reported 10 and 3 h/wk of vigorous physical activity, 43 and 40 h/wk of moderate physical activity, and 12 and 6 h/wk of walking, respectively. Many modern cultures have approximately one-third the number of daily steps as taken by Amish (23). On average, non-Amish adults report an average of 5,117 steps per day, and were separated into four groups: “very active” (6,805 steps/day); “somewhat active” (5,306 steps/day); “somewhat inactive” (4,140 steps/day); and “very inactive” (3,093 steps/day). Additionally, Church et al. (88) estimated that occupational energy expenditure decreased by >100 calories in both genders over the four decades. They concluded that a 100-kcal reduction in occupational energy expenditure would account for much of U.S. weight gain over the past half century. Myers, McAuley, Lavie, Despres, Arena, and Kokkinos (348), all experts in the field, support the high levels of physical inactivity in their quotation: “Current physical activity patterns are undeniably the lowest they have been in human history, with particularly marked declines in recent generations and future projections indicate further declines around the globe . . . Non-communicable health problems that afflict current societies are undeniably attributable to the fact that PA patterns are markedly different than those for which humans were genetically adapted” (8, 127, 356, 366).

C. Approximately 86% in the U.S. Do Not Meet Physical Activity Guidelines: Physical Inactivity Is Now Pandemic

Accelerometers to measure movement have superseded recall self-reporting and pedometers for validity recording of physical activity (509). We used accelerometer data in Troiano et al. (505) as a basis for estimating the prevalence of physical inactivity in the U.S. at ~86%, making it one of the highest, if not the highest, unhealthy condition in the U.S. With the U.S. population at ~325 million people and using the Troiano et al. (505) percentages of inactive humans, we estimate that >280 million in the U.S. are not meeting the U.S. physical activity guidelines for minimal physical activity to improve health.

It is unquestionable that physical inactivity has become a global health issue. Kohl et al. (254) concluded, “Physical inactivity is pandemic, a leading cause of death in the world, and clearly one of the top four pillars of a noncommunicable disease strategy. However, the role of physical activity continues to be undervalued despite evidence of its protective effects and the cost burden posed by present levels of physical inactivity globally.” Worldwide, percentages are similar to the U.S., as only 6 and 4% of English men and women, respectively, met requirements for 30 min of moderate or vigorous on at least 5 days/week with accumulated bouts of at least 10 min (86). Limited-available nonaccelerometer data suggest that >30% of the world’s population does not meet the minimum U.S. recommendations for physical activity (541). Therefore, >2.5 billion would be considered inactive by U.S. physical activity guideline standards. Lee et al. (287) estimate that 6–10% of worldwide deaths from noncommunicable diseases are due to physical inactivity. The incidence of physical inactivity is high, and unfortunately, current trends do not suggest a reversal is on the horizon. Taken together, the underappreciation of physical inactivity as a health threat could be described as “stealth” pandemic.

In the above context, the presentation of physical inactivity to alter behavior could use one of two generalized approaches. One proposal by Rose (431) is that structure (pertaining to social institutions and norms that shape the actions of individuals) may be more effective than from agency (pertaining to an individual's capacity to make the choice to act). Readers are directed to papers favoring the structure option (331). For example, Adams et al. (1) mention the example that if packaged foods had reduced salt content, then individuals would not have to “consciously engage with any information or actively change their behavior.” We speculate that an inactivity analogy could be providing safe bike paths (structure) instead of governmental policy recommendations to exercise 30 min most days of the week.

D. Annual Costs of Physical Inactivity in the U.S. Are Estimated Between $131 and $333 Billion and Are Rising

11.1% of aggregate health care expenditures in the U.S. during 2006–2011 were associated with physical inactivity according to Carlson et al. (75) at the CDC. They conservatively estimated the inactivity cost to be $131 billion. Carlson et al. (75) state that all previous cost estimates to U.S. health care were fivefold underestimates, implying the newest estimate is also an underestimate, and state that “this study did not estimate indirect costs, which include lost productivity from premature death and disability associated with illness, nor does it address the costs in the institutionalized population that may be associated with inadequate levels of physical activity. Other studies indicate that their estimates of physical inactivity are due to conservative methodologies (119). On the other hand, we estimated 2014 U.S. health care costs to be ~$333 billion (11.1% × $3 trillion of the total U.S. health care costs in 2014) (82b). Future studies that consider these additional costs may improve estimates of the economic burden of inadequate physical activity. Nevertheless, this study found that inadequate physical activity is associated with a significant percentage of health care expenditures in the U.S.” They also found health care expenditures were very similar for inactive adults in three independent studies. The costs were 26.6% for 51,000 U.S. adults who were 21 yr of age or older (75), 26.3% for 7,004 Australian women aged 50–55 yr old (68), and 23.5% for 5,689 individuals, 75% of whom were 40 yr or older in a Minnesota health plan (407). On the other hand, costs of physical inactivity in a total of 142 countries were “conservatively estimated” to be direct and indirect costs of $67.5 billion (international dollars) worldwide in 2013 (119), which is about one-half the estimate given above for the U.S. alone by Carlson et al. (75).

E. Evidence Exists That Physical Inactivity Actually Causes Risk Factors, Leading to Increased Morbidity and Mortality

This section continues theme 2 by providing detailed evidence of the link between physical inactivity, epidemiological evidence, and risk of morbidity and mortality. As mentioned, Morris et al. in 1953 (342) reported the novel observation that bus conductors in London double-deck buses, who had to continually climb stair to collect fares, had ~30% less coronary heart disease, were older when the disease was diagnosed, and had a lower death rate than bus drivers who sat on the same buses. In 2010, Blair et al. (42) wrote “the research field of exercise epidemiology that was initiated by Morris nearly 60 years earlier had grown to an impressive body of physical inactivity and low cardiorespiratory fitness (CRF) are major causes for increased physiological dysfunction, morbidity, and mortality. Blair et al. (41) noted in 40,000 subjects from the Aerobics Center Longitudinal Study that low CRF is a stronger predictor of mortality than any other risk factor.

Physical exercise is not an actual causal mechanism of chronic diseases, but rather physical activity “protects” or is a therapy for diseases/conditions caused by physical inactivity (52). A 72-page review on prescribing exercise as a therapy for chronic diseases is available from Pedersen and Saltin (390). Physical inactivity, on the other hand, is one of numerous actual causes of 35 chronic diseases/conditions (55) (FIGURE 3).

Many chronic diseases are polygenic, so it is not unexpected that more than a single mechanistic pathway may cause a polygenic disease. For some diseases, including six of the more prevalent chronic diseases discussed below, percent increases associated with physical inactivity range between 20 and 45%.

F. Why Extend Epidemiology to Inactivity-Induced Pathophysiology?

One simple answer is that despite all the outstanding epidemiological studies, they have not stopped the physical inactivity pandemic. The strong dogma that physical activity prevents chronic disease has not reversed the public health challenge of the physical inactivity pandemic. For example, <50 million out of 325 million in the U.S. population meet 2008 U.S. guidelines for minimal public health.

Pathophysiology is defined here as the structural and functional manifestations of a disease. One clinically significant factor is that structural changes are often irreversible. Thus, to prevent a chronic disease in the first place, i.e., primary prevention, structural changes must be prevented. Prevention of chronic physical inactivity is the primary preventer of chronic diseases. FIGURE 4 illustrates this concept.

As mentioned, the strong cycle of physical inactivity induces dysfunction and pathophysiology, causing at least 35 chronic diseases/conditions, which in turn results in greater levels of physical inactivity. The rapidity and severity of the response to physical inactivity is startling and is exemplified in the 1968 Dallas Bed Rest Study (443). Five healthy males underwent 20 days of continuous bed rest. The percentage declines in mean maximal physiological values for the subjects during maximal running on a treadmill were very significant for a 20-day period. V̇o_2max (aerobic fitness) fell 27%. Underlying the decrease were decreases of 11% decrease in heart size, 26% decrease in maximal cardiac output, and 29% decrease in maximal stroke volume, but no significant changes in maximal heart rate or in mean arterial-venous O₂ difference were noted. Taken together, the percentage size of the decrements in mass and function of the cardiopulmonary system in 20 days emphasizes the concept that the human body was built to rapidly adapt to a dysfunctional state with very short periods of physical inactivity. Remarkably, the transition from normal function for aerobic fitness in 20-yr-old, healthy men (physiological state) to low aerobic fitness for that age (physiological dysfunction) was equivalent to 30 yr of normal aging from 20 to 50 yr old. Twenty days of continuous bed rest places these individuals in a pathophysiological state leading closer to disease. These bed rest studies have been extended to present everyday living. For example, Pedersen et al. (263) had young Danish men reduce their daily step counts from 10,501 to 1,344 for a 2-wk period. A startling 6.6% decrease in V̇o_2max suggests physical inactivity is a major environmental component. Unfortunately, the gene mechanisms underlying the decline in V̇o_2max with aging and/or inactivity are virtually unknown. To further complicate this area of research is the fact that V̇o_2max is regulated by multiple organ systems, each with unique contributions to V̇o_2max.

Mechanisms of disease can be defined as defects in processes that trigger specific pathologies. Joyner and Green (230) comment that approximately half of the protective effects of physical activity are accounted for by traditional risk factors such as reductions in blood pressure and blood lipids. They suggest the missing one-half is due to the lack of knowledge to understand how the protective effects of physical activity are linked to health benefits, knowledge that is still lacking. We suggest that the missing link may be related to the concept that different molecular adaptations produce health-beneficial consequences of physical training and physical inactivity. In 2000, Booth et al. (52) proposed “the biochemical, molecular, and cellular mechanisms of physical inactivity will provide the scientific foundation for appropriate individual prescription of physical activity for health.” The topic is discussed in greater detail in Booth et al. (55).

G. General Disappointment in Gene Variants Becoming Predictive as Medicine Therapies to Prevent Chronic Diseases Caused by Physical Inactivity

This section provides the background for theme 3 regarding gene-environment evidence. Joyner and Pedersen’s review (231) notes disappointment that the promise that simple gene variances have not emerged for common diseases by suggesting “a second key example was the sequencing of the entire human genome announced in 2001 and the idea that a limited number of genetic variants would emerge and explain common diseases like cancer, hypertension, atherosclerosis, diabetes, etc.” For example, no significant genetic risk score to the incidence of total cardiovascular disease was observed for 101 single nucleotide polymorphisms in a prospective study of 19,000 initially healthy white women (383). Pedersen (392) commented that “findings such as those reported by Seshadri et al. (459) reinforce the futility of using individual genetic risk profiling for AD [Alzheimer’s disease] beyond collecting information on age, sex, family history, and APOE status.” A 2016 update by Talwar et al. (483) is, “therefore, these identified genetic markers individually or in combination have little or no clinical (predictive or diagnostic) utility in predicting AD [Alzheimer’s disease] risk.” The odds ratio for developing dementia in APOE ɛ4 non-carriers were twice as high in non-exercisers than in exercisers. However, APOE ɛ4 carriers found no difference in the odds ratio for dementia development was present between non-exercisers and exercisers (143). One conclusion in a 2013 issue of Diabetes Care for the status of genetic screening for T2D risk is summed by its statements, “however, available data to date do not yet provide convincing evidence to support use of genetic screening for the prediction of T2D . . . Genetic testing for the prediction of T2D in high risk individuals is currently of little value in clinical practice” (311). In addition, some outcomes of the Functional Single Nucleotide Polymorphisms Associated with Human Muscle Size and Strength study or FAMuSS were as follows: 1) “individual genetic variants explain a small portion of the variability in (511) muscle strength and size response to resistance training (393). 2) Genetic variants that were examined in the resistance training study only <1-12% of body composition and cardiometabolic markers in habitual physical activity levels, “suggesting these traits are highly polygenic with many loci contributing a very small proportion of the variation, and these phenotype-genotype associations were often sex specific” (393). Furthermore, in ~6,400 individuals of European descent and over 65 yr of age, no significant gene-variant associations were observed with lower body strength (325). The outcome contrasted with handgrip strength, which revealed an association with molecular targets in ~27,000 individuals >65 yr old, whose genes were of European descent (325). Taken together, the above verifies the quotation “simple genetic answers have not emerged for common diseases” (231).

Only one human gene variant has been identified that is related to physical inactivity. A gene variant in the FTO (fat mass and obesity-associated protein) gene only expresses its negative health effect of increased probability of obesity in the presence of physical inactivity. One FTO risk allele that is associated with obesity is 27% higher in physically inactive adults (243). Demerath et al. (111) stated that the FTO variant is the strongest common genetic susceptibility locus for obesity yet discovered (118, 161, 456). Thus we interpret that physical inactivity is a strong environmental stimulant of one gene variant in the FTO gene variant for obesity.

H. Polygenic Heritable Factors Regulate Sedentary Behavior

The existence of genes for sedentary behavior has indirect, strong support from 1) twin studies which identified heredity as a source of inactivity between pairs if twins (113), 2) selective breeding for the phenotype of physical inactivity in rats (423), and 3) comparisons between naturally low and high levels of voluntary running. FIGURE 5 provides an overview that evolution can be used as a foundation to speculate as to how physical inactivity could explain a genesis for observed interactions among genes, environment, and chronic diseases.

I. Darwinian Medicine Application to Fitness

In our view, Darwinian medicine could suggest that survival of the species depends on rapid responses to new changes in the environment to increase the probability to survive, which was introduced earlier as theme 8. From this, we could suggest that a rapid transition between endurance and strength fitness might have been a survival advantage, as the two fitness phenotypes differ in function. We define endurance exercise as continuous submaximal contractions of large muscle groups, while strength exercise is defined as producing near-maximal forces for a short period (seconds) in any skeletal muscle group. Holloszy and Booth (217) noted that hypertrophied muscles from strength training have minimal, or no, increases in skeletal muscle mitochondrial concentration versus endurance-trained skeletal muscle having increased mitochondrial concentrations without hypertrophy. If one approximates the time for each contraction (repetition) against a near-maximal load to be ~3 s, then as an example, training a muscle for 3 sets with 8 repetitions per set would be ~72 s, and if trained 2 times/week, weekly duration of strength training would be ~2 min/wk. This duration is ~1% of the duration of 150 min/wk for endurance training in the U.S. physical activity guidelines. Taken together, the actual duration of muscular contraction may differ by 100-fold between two major modalities of exercise training for health adaptations to physical activity.

Along these lines, Coffey and Hawley (92) discuss the phenotype differences between endurance versus strength training, reporting that differing signaling pathways are not only producing the two phenotypes, but moreover that “it is likely that multiple integrated, rather than isolated, effectors or processes are required to generate the interference effect,” whereby maximal strength development is impaired in individuals who train using both strength and endurance workouts, as compared with strength training alone. Darwinian medicine could suggest that survival might have depended, in part, on the rapidity to transform from endurance to strength optimization, or vice versa. Such speculation could contribute to why physical inactivity is associated with rapid skeletal muscle atrophy (493) (for strength training) and rapid decline in mitochondrial concentrations (343) (for endurance training). Both genetically optimal skeletal muscle endurance and size/strength require differing molecular signals to produce different phenotypes.

While Darwinian Medicine concepts are applied to the gain in either endurance or strength fitness, the gain of one type of fitness is often associated with a decline in the other type of fitness because signaling pathways inducing both produce conflicting phenotypes. For example, endurance running requires small fiber diameters of skeletal muscle fibers to limit diffusion distance for optimal oxygen transport, while muscle strength is associated with large-diameter fibers to increase force per fiber. A speculative hypothesis would be that the rapid time required to increase endurance type of fitness for survival purposes in a new environment requiring endurance could be dependent on the rapid loss (increased degradation rate) of skeletal muscle diameter to obtain the short oxygen diffusion distance (see sect. IXB for detailed discussion).

J. Environmental Manipulation of Genes by Physical Inactivity and Possible Link to Epigenetics

Pima Indians provide an example of a human population highly predisposed to obesity and T2D. Although they share a common genetic background, they have come to reside in two geographical locations upon separation ~1,000 yr ago, with those residing in Arizona adopting a Western lifestyle of physical inactivity and diet (454). Arizona Pima Indians have developed one of the world’s highest prevalence of T2D (248). In contrast, the Mexican Pima Indian population maintained their historical, relatively low T2D prevalence (413), related to a physically active lifestyle that included wood milling, nonmechanized farming, livestock breeding, security guarding, construction, mining, and homemaking (136). Arizona Pima Indians were estimated to to expend ~500–600 kcal/day fewer than their Mexican counterparts (136). Although DNA sequences most likely did not change in the 1,000-yr separation, epigenetic changes likely occurred in the Arizona Pima population due to their lifestyle changes to less physical activity and a Western diet. Such a supposition could be based on what Noble’s reference (363) to Waddington who “demonstrated the inheritance of a characteristic acquired in a population in response to an environmental stimulus.” It is a reasonable notion that physical inactivity could induce changes in gene expression by epigenetic mechanisms. More recently, epigenetics was described as a molecular event that involves heritable changes in gene expression. Epigenetics encompasses alterations in gene expression without nucleotide alterations in the DNA coding sequence that are heritable through cell division. These modifications include histone modifications and DNA methylation, but new mechanisms suggest that other molecular events, such as noncoding RNAs, are implicated in several epigenetic mechanisms.” One example of this is Alibegovic et al. (6), who noted a trend toward greater DNA methylation of PPARGC1A in the vastus lateralis muscle after 10 days of bed rest, which could contribute to the impaired expression of PPARGC1A.

A. Bed Rest

Bed rest represents an extreme level of physical inactivity. Typically, subjects participating in bed rest studies only move the upper limbs, while removing all weight bearing against gravity from the legs. In the early 1950s, the standard of care for a myocardial infarction was bed rest. However, President Eisenhower’s personal cardiologist, Paul Dudley White, argued against bed rest after President Eisenhower’s heart attack (277). He prescribed early ambulation, which later was credited for saving, or at least prolonging, the Presidents’ life. Interestingly, cardiac rehabilitation was developed around the same time after acute myocardial infarction, where before this, the standard of care was bedrest and inactivity. Interestingly, bed rest was used in early experiments to better understand the deleterious effects that occur during spaceflight, while on land. The strength of bed rest as physical inactivity model is that it permits a more homogeneous experimental treatment, i.e., the experimenter can control for subject variability and can limit amount of movement. The first human bed rest study with major impact was the Dallas bed rest study by Saltin et al. (443), described above. After only 20 days of continuous bed rest by healthy young men, remarkable decreases occurred in V̇o_2max, maximal total heart volume, maximal stroke volume, and maximal cardiac output. One weakness of conducting bed rest studies is the expense of conducting these types of studies and the deleterious health outcomes common to the subjects involved. Furthermore, bed rest studies are clinically relevant to diseases/accidents that require bed rest to heal the primary disorder.

B. Spinal Cord Injury

Spinal cord injury approaches the most absolute form of physical inactivity. Within spinal cord injury, there are various forms and severities of spinal cord injury, such as paraplegia or quadriplegia (246). The location that the lesion or damage occurs within the spinal cord will determine the loss of function. If anything can be said positive about the tragic condition of quadriplegia, it is that this condition offers insights into the effects of inactivity in the absence of innervations. One experimental weakness of the condition is the heterogeneity in human subjects due to high variability in the severity of spinal cord injury. Several reviews on the model exist, including non-human primates for translational research to the human condition (365) and using the rat as the preclinical model (130).

Other types of skeletal muscle denervation are present. Aging is associated with loss of neuromuscular junctions (223, 438) in sarcopenia. Loss of motor unit numbers is slowed by life-long high-intensity physical activity (402), implying a role for inactivity in motor unit loss. A part of a recent review (93) considers molecular mechanisms during sarcopenia.

C. Spaceflight

Spaceflight is deleterious to many organ systems due to the lack of gravity (319). The uniqueness of the near-zero gravity form of physical inactivity in near-orbital flight is that it enlightens the role of lack of gravity in physical inactivity adaptation on Earth. Even though astronauts can perform physical activity in near-orbit in space under near zero gravity conditions, many of the positive physiological adaptations from exercise are not conferred due to the lack of Earth’s gravity. For example, running on a treadmill in near-zero gravity does not prevent bone loss by itself, as there is no mechanical stimulus developed from a weightless body on lower extremity bones on a treadmill belt in weightlessness. The skeletal system does not experience weight bearing and therefore undergoes bone decalcification (283, 518). The strength of spaceflight as a model of physical inactivity is due to being able to separate the factor of gravity from other exercise responses. Some weaknesses are that due to the limited numbers of individuals who go into space and the high demands placed on the astronauts in space on short missions, limited data are available on short-duration space flights for studying this form of inactivity. Additional, Morey-Horton and Globus (341) reviewed ground-based animal models of space flight.

D. Limb Immobilization

E. Sitting

F. Intermittent Breaks in Physical Inactivity

Edgerton’s laboratory hindlimb suspended rats for 1 wk such that soleus and gastrocnemius muscles became non-weightbearing (210). One group underwent the countermeasure of intermittent breaks from non-weightbearing, and the authors noted that high-load exercise at four intervals spaced over 12 h daily prevented about half of soleus muscle atrophy and hypertrophied the gastrocnemius muscle. In a subsequent study, the Edgerton laboratory (205) noted that non-weightbearing rats walking 40 min/day (10 min every 6 h) halved the amount of atrophy in the soleus muscle and mitigated atrophy in the gastrocnemius (185). In a separate study, the Booth laboratory (109) provided a non-weightbearing group 2 h daily centrifugation for 1 wk at one of three gravity levels. Soleus muscle masses were 48, 56, and 65% of control masses at 1, 1.5, or 2.6 G force, respectively, of the atrophy produced with continuous non-weightbearing. They followed up this noting that rats undergoing four 15-min periods of centrifugation at 1.2 G, spaced over a 12-h interval in the sleep period of the day, prevented 67% of soleus muscle atrophy (108).

G. Decrease in Daily Step Numbers

A. Accelerometer Data on U.S. Children and Adolescents

The data below from several studies indicate physical activity falling in childhood, implying the inverse that physical inactivity is increasing at the same time. Trost et al. (506) concluded that physical activity drops rapidly during childhood and adolescence, as evidenced by a 40% decline in moderate-vigorous physical activity going from grades 1–3 to 4–6 (FIGURE 6).

The U.S. Physical Activity Guidelines designate ≥60 min of daily moderate- or greater-intensity activity on 5 of 7 days/week is required for health. Both sexes also fell below the U.S. Guidelines for the chronological ages between the groups for grades 7–9 and 10–12. In addition, participation in 20-min continuous bouts of physical activity was low to nonexistent.

Troiano et al. (505) noted the majority of U.S. children and adolescents did not meet the U.S. Guidelines for physical activity. In 6–11 yr olds, 65 and 51% of females and males, respectively, were physically inactive by the U.S. Physical Activity Guidelines, while 12–19 yr olds exhibited an alarming 96 and 92% physically inactivity rates of females and males, respectively. More importantly, these percentages are high compared with other risk factors for cardiovascular disease. For comparison, 21% of children and adolescents exhibit at least one abnormal cholesterol measure [low high-density lipoprotein (HDL) cholesterol, high total cholesterol, or high non-HDL cholesterol] (357). The above comparison suggests that physical inactivity is an unappreciated risk factor for chronic disease.

Wolff-Hughes et al. (547) uniquely analyzed data from the US 2003–2006 National Health and Nutrition Examination Survey (NHANES). They employed wrist accelerometers on 3,700 U.S. youth (FIGURE 6). Moderate to vigorous physical activity decreased ~67 and ~60% in U.S. girls and boys, respectively, from the age of 6 to 19 yr old. After 8.7 and 10 yr of age, 50% of girls and of boys, respectively, engaged in <60 min of daily, intermittent moderate-vigorous physical activity. Rääsk et al. (409) also noted using accelerometry that pubertal boys started to be less active in their pubertal period. Taken together, the above four reports showed a significant decline accelerometer-detected physical activity in youth over time. Furthermore, more than half of U.S. youth did not perform sufficient daily physical activity, according to U.S. Physical Activity Guidelines. This has been extended to other countries, as Hallal et al. (197) reported that 80% of 13–15 yr olds in 105 countries averaged <60 min of moderate to vigorous, daily physical activity, with girls being less physically active than boys.

Despite the epidemic levels of physical inactivity, little is known about the underlying genetic and biological mechanisms that may contribute to the regulation of physical inactivity behavior. Some of what we know has emerged from physical activity studies. Garland and Carter (172) reviewed that physiologists have historically recognized that animals living in extreme environments show “clear examples of evolutionary adaption because of the presumably intense past selective pressures.” Swallow et al. (480) reported that an ~75% increase in distance run in voluntary running in wheels after 10 generations for high voluntary running. While cultural and social pressures definitely influence physical activity in humans, they do not regulate these behaviors 100%, and the estimated genetic component for physical inactivity has been estimated at between 20 and 80% in animal and human studies (247). We tested whether selective breeding would reveal the characteristic of low voluntary running. After nine generations, female and male rats selected for lowest distance of running in wheels exhibited an 87 and 84%, respectively, decrease in wheel running distance, as compared with wild-type rats (422). Our findings provide supporting evidence for a genetic component influencing sedentary behavior, that we have seen continue in future generations of rats selectively bred for the primary characteristic of low voluntary running distance (67, 422, 423, 425, 440, 441).

The above studies may also have evolutionary significance. The studies described above (409, 505, 506, 547) exhibit a similar age for declines in moderate to vigorous physical activity. Voluntary physical activity also reaches its lifetime highest value around the age of puberty in Wistar female rats bred to be high voluntary runners (498). Next, we will extend information from theme 9, introducing the topic of voluntary running behavior peaking early in the lifecourse. We speculate that evolution may have concurrently matched the ages, puberty, and maximum voluntary physical activity to increase the probability of successful mating to the next generation. Inherent gene regulation may trigger the initial decline from peak lifetime voluntary physical activity. As the clinical diagnosis of most inactivity-produced human chronic diseases occurs post-puberty, even in cases where chronic disease onset is pre-pubertal, reproductive viability is usually maintained into later adulthood. Thus reproduction will be successful in transferring genes to the next generation independent of the inactivity status of the youth. Consequently, it is unlikely that natural selection would extinguish inherited genes for physical inactivity.

B. Childhood Activity Sets the Stage for Adult Health

Some evidence exists to support the assertion that physical inactivity during youth is associated with a higher probability of lower CRF, bone strength, skeletal muscle mass/strength, and other cardiometabolic factors throughout the remainder of life (146). Furthermore, the likelihood of these factors being retained later in life increases through childhood to adolescence (158, 160).

For example, a 26-yr follow-up study of 1.1 million Swedish men who had mandatory military conscription and physicals noted that 18-yr-old recruits that has a combination of low exercise capacity and low muscle strength had 23% higher hazard ratios for vascular events 26 yr later (7).

Forrest and Riley (158) contend that the health of the population at later ages is affected by modifiable precursors of many common chronic disorders that arise during childhood. Some mechanistic evidence supports this contention. For example, voluntary wheel running after weaning reduced diet-induced obesity (381). Six-week exposure to early voluntary running exercise prevented diet-induced obesity in susceptible rats while they continued to consume an obesogenic diet, but not engaging in voluntary running. For hypothalamic peptides, the 6-wk voluntary running, 7-wk sedentary rats had a 55% greater mRNA expression of arcuate nucleus proopiomelanocortin, as compared with sedentary rats without voluntary running, suggesting a hypothalamic contribution to their sustained obesity resistance.

A. Physical Inactivity Increases Cognitive Dysfunction Throughout the Lifespan

Cognitive function is defined as the intellectual processes by which an individual becomes cognizant, perceptive, or understanding of ideas. The process engages all aspects of perception, reasoning, remembering, and thinking. Cognitive decline is associated with aging, including Alzheimer’s disease and other forms of dementia. Thus cognitive function is arbitrarily divided into two time frames of developing and then declining cognition. Cotman and co-workers (102, 221), Kramer et al. (404), van Pragg et al. (524), and others have noted that increased physical activity drives cognitive development of specific brain areas. On the other hand, physical inactivity is associated with reduced cognitive abilities. Low physical activity in older women increased risk of cognitive impairment, Alzheimer's disease, and any type of dementia by 72, 100, and 59%, respectively (279). Laurin et al. (279) concluded: “Regular physical activity could represent an important and potent protective factor for cognitive decline and dementia in elderly persons.”

Similar findings show physical activity improvements on cognitive health is prevalent across lifespan (214). Physically active Dutch men between 15 and 25 yr of age had a lower age-related decline in informational processing ability compared with individuals physically inactive over the same age range (115). Likewise, women had a lower likelihood of cognitive dysfunction later in life if they were physically active either early in life or became active after being teenagers (335). Importantly, physical activity during the teenage years appeared to strongly relate to improved cognitive function and to decreased cognitive impairment later in life (335). Increased physical activity level in children (age 4–18 yr) is strongly associated with increased achievement, developmental level/academic readiness, intelligence quotient, perceptual skills, and verbal and math test scores (137). Children and adolescents with low physical activity levels have lower cognitive performance as compared with physically active children (70, 81, 83a, 84, 122, 399), and physical activity may enhance children’s executive function, such as making decisions and prioritizing tasks, managing time efficiently, and organizing thoughts and activities (500).

Comparable relationships between cognitive function and physical inactivity have also been found in older adults. In ~18,000 women aged 71–80 yr, lower levels of long-term, regular exercise were related to decreased cognitive function and a 20% increased risk of cognitive impairment (538). Likewise, the incidence of dementia rose from 13.0 per 1,000 person-years to 19.7 per 1,000 person-years with greater physical inactivity (<3 bouts of exercise/week) in a >65 yr-old group (276). Physical activity levels were inversely related to dementia in men and women who were 65 yr old and older (398). Lower V̇o_2peak was related negatively with preserved cognitive function during a 6-yr period in 349 subjects greater than 55 yr old (20). Similarly, 24 wk of resistance training positively affected multiple measures of cognitive function in 65–75 yr old males (80). However, whether the improvements in cognitive function may be dictated by physical activity or fitness is unclear. In a ~1,300 subject meta-analysis, Etnier et al. (137) concluded that cognitive performance is positively associated with physical activity, but that empirical evidence to support a relationship between cognitive performance and aerobic fitness was lacking. Other meta-analytic reviews have observed similar findings (94, 142). Conversely, Voss et al. (525) concluded “CRF is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.” They also caution that it is still necessary to measure both physical activity and CRF fitness because results for physical activity could be due to higher fitness among high responders to regular physical activity.

Minimal information is available for mechanisms by which physical inactivity initiates mechanisms causing cognitive dysfunction. However, physical activity could be used to build hypotheses for mechanisms by which exercise rescues cognitive dysfunction. A limitation of such a suggested approach would be that for some of the few known inactivity mechanisms, these signaling pathways are not always the reversal of exercise signaling pathways (470). A further limitation is “the underlying mechanisms for the positive effects of exercise on wellbeing remain poorly understood” (199). Thus only a limited number of exercise mechanisms are available to use in a strategy already limited by lack of inactivity mechanisms being a simple reversal of exercise mechanisms. For example, physical activity increases dentate gyrus neurogenesis, which reviews interpret as associating with cognitive preservation (312, 517). Other reviews (102, 313, 524) highlight that growth factors [brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF), insulin-like growth factor I (IGF-I)] transmit downstream exercise signaling to enhance hippocampal plasticity and related memory benefits (91, 135, 313, 521, 523).

We could hypothesize that physical inactivity with aging may lower V̇o_2max to a level that may limit exercise intensity and reversibility of low neurogenesis, plasticity, cognition, BDNF, VEGF, and IGF-I in the dentate gyrus in old humans. For example, in older (60–77 yr), sedentary, healthy males and females, after 12 wk of progressive interval training, regional cerebral blood flow tended to increase in those near 60 yr old, but, in contrast, decreased in those in the 70–77 yr old age range (314). In addition, several correlations were reported, including three determinations (percent increase in hippocampal regional cerebral blood flow, percent increase in hippocampal volume, and percent increase in the Complex Figure Test, a measure of long-term memory), that all correlated with the increase in oxygen consumption obtained at anaerobic threshold. In addition, increase in hippocampal volume and Complex Figure Test were both correlated with the increase in hippocampal regional cerebral blood flow, and the increase in hippocampal regional cerebral blood flow was correlated with the increase in hippocampal volume (314). We speculate that as humans age from 60 to 77 yr old, age-associated declines in cardiovascular fitness will increase the relative work load intensity needed for the absolute oxygen consumption value that is required for the beneficial exercise effects on the hippocampal functions/structure, potentially decreasing the ability to work at the higher relative work intensities. In a second report (380) of older subjects (male and female subjects, age 60–72) with inadequate physical activity levels (<2–3 exercise events/month) and high levels of the pro-inflammatory biomarker IL-12p40 (one of two subunits of IL-12), at the end of a 6-yr period, the subjects had smaller volumes of hippocampus and lateral prefrontal cortex associated with greater declines in Mini-Mental State Examination test than two physically inactivity individuals with low values of IL-12p40 (380). The authors concluded that “these patterns of data suggested that inflammation was particularly detrimental in inactive older adults and may exacerbate the negative effects of physical inactivity on brain and cognition in old age.”

Others have hypothesized additional downstream mechanisms by which physical inactivity could produce cognitive decline. For example, sedentary rats had higher oxidative stress, as determined by protein carbonyls, and decreases in superoxide dismutase-1, glutathione peroxidase, as well as decreases in p-AMPK and PGC-1α proteins in hippocampus than endurance-trained rats by treadmill running (320). In humans, greater amyloid deposition in brain was found in sedentary, cognitively normal individuals (aged 55 to 88 yr old), as compared with those who exercised regularly (206), implying a negative role for physical inactivity in Alzheimer’s disease. The same investigators also tested subjects who were both cognitively normal and Apolipoprotein_E(APOE ϵ4)-positive individuals (aged 45 to 88 yr). Physically inactive subjects with APOE ϵ4 genotype also had greater amyloid than the exercised subjects (206). Furthermore, in a large epidemiological study (364), physical inactivity, as a risk factor for Alzheimer’s disease, exceeded each of six other modifiable risk factors, including depression, diabetes, low education, hypertension, obesity, and smoking.

B. A Lack of True Physical Inactivity Studies in Healthy Adults

While most reports associate cognitive benefits with increases in physical activity from a sedentary/baseline measure, very few reports have shown direct relationships between reductions in physical activity and cognitive function. One physical inactivity model previously discussed is spaceflight, and according to Strangeman (474), available evidence is inclusive of supporting or denying the existence of specific cognitive deficits during long-duration spaceflight. Cognitive effects of bed rest are also not conclusive and remain to be established (260, 302, 303). Furthermore, relatively few animal studies have analyzed how reductions in physical activity influence cognitive abilities. This paucity in research directly studying the mechanisms by which physical inactivity may promote decreases in cognitive function is an important research gap to fill, given the magnitude of its clinical consequences and how reductions in physical activity affect cognition.

C. Mechanistic Links Between Physical Inactivity and Cognitive Impairments

Seminal work by van Praag et al. (515) reported that voluntary wheel running (VWR) increased survival of nascent cells in dentate gyrus, a hippocampal region important for spatial recognition in 3-mo-old mice. Similar findings show that following 10 wk of voluntary wheel running, spatial pattern separation was strongly correlated with increased vasculature and neurogenesis in the dentate gyrus of 3-mo-old mice (104). Improvements in brain blood flow are also associated with improved cognitive performance. Underlying greater blood flow with higher brain angiogenesis that was associated with enhanced improvement in water maze time and retention of spatial-reference memory (516). VWR increases densification of blood vessel density, capillary perfusion, and blood flow in the motor cortex in rats (40, 479), potentially through increases in angiopoietin 1, endothelial proliferation, density of microvessels, and VEGF protein (120).

Many of the physical inactivity-related decreases in cognitive function have been associated with local and systemic expression of growth factors. For example, BDNF, particularly in the hippocampus, has been associated with many of the positive effects on cognitive enhancement (101, 353). Rodent studies demonstrate that BDNF protein levels increase progressively with regular VWR (32). Conversely, stopping VWR decreases hippocampal BDNF and BDNF/NT-3 growth factor receptor (TrkB) mRNAs (542). Additionally, BDNF promotes long-term potentiation by improving synaptic plasticity in the hippocampus of BDNF knockout mice (382), an analog of learning and memory. Improvements in long-term potentiation and synaptic plasticity (304, 513), as well as enhancements in dendritic arborization and synaptic plasticity in the hippocampus (114, 473), occur in response to physical activity.

Similarly, IGF-I is another critical growth factor for neuroprotection and brain health. Like BDNF, IGF-I levels are decreased in the circulation of sedentary compared with physically active animals (504). Both treadmill running and systemic infusion of IGF-I enhance the number and survivability of hippocampal BrdU+ cells (306, 504). Intracarotid infusion of IGF-I mimicked increases in neuronal c-fos and BDNF in the hippocampus observed with treadmill running, which was reversed by infusion of an anti-IGF-I antibody (76). Additionally, anti-IGF-I antibody treatment abrogated the protective effects of treadmill running on spatial memory in mice with hippocampal injury (77).

In the aforementioned studies on rats selectively bred for low voluntary wheel running (67, 422, 423, 425, 440, 441), after eight generations, selected low voluntary runners exhibited a 10-fold decrease in wheel running distance, as compared with rats simultaneously bred for high voluntary running, and roughly 4-fold less running distance than the outbred founding population (422). Genetically engineered rats for the phenotype of low voluntary running were linked to depressed function in the mesolimbic dopamine system, a system central to functions such as motivation, reward, and learning, and co-segregate with the selection for low voluntary running behavior (423, 425). Additionally, transcriptomic analysis of the nucleus accumbens, an important region of the brain containing the mesolimbic dopamine system, has identified inherent decrements in neuronal maturation in rats selected for low running behavior (425). Similar results were found in mice bred for high voluntary running and also implicate dopamine and certain midbrain structures as being important in the evolutionary regulation of physical activity (323, 332, 419). Although strong evidence exists to support a genetic contribution to physical activity regulation, other biological (nongenetic) and environmental factors must be investigated to completely understand the precise mechanisms regulating this complex and essential behavior.

Interestingly, mechanisms of activity may be linked to evolutionary changes. Ruben and Bennett (437) postulated in 1980 that the selection of burst-speed physical activity by animals might have contributed to the co-selection of “cephalization in protovertebrates and the appearance of vertebrates themselves.” They mention that adult invertebrate chordates have both a low degree of cephalization and are “relatively sedentary.” They then postulate that vertebrate cephalization might have developed during selection to fulfill the need for increased sensory and locomotor control as their more active lifestyle evolved. Overall, while epidemiological and mechanistic insight suggest that physical inactivity hastens the decline in cognitive function, this decline can be lessened, or even potentially reversed, by exercise. However, many questions remain unanswered concerning the best strategies to minimize these deficits.

In addition, it should be noted that physical inactivity in children in school can have a negative impact on cognitive ability and academic performance. Hillman and others have published numerous timely reviews in this emerging area of research (123, 213, 234).

D. Physical Inactivity Increases Risk of Depression and Anxiety

Depression is a leading cause of disability within developed nations (307), and by 2020 depression is predicted to be the second leading cause of human disability, next to cardiovascular disease (346). Depression has a lifetime prevalence of 16%. Furthermore, depression’s cost yearly in the U.S. is $210 billion and is growing (187). Similarly, the prevalence of anxiety is 10% in the general population, and it has many parallel symptoms and treatments as does depression. Both anxiety and depression are linked with many other disease risks. Recent research suggests that physical inactivity may be an actual cause of depression (385). Additionally, significant attention has been focused on the potential role of exercise in preventing and/or managing depression and depressive symptoms (378).

In general, data from observational and intervention studies hint that physical inactivity has similarities to depression and depressive systems (488). More than 100 population-based, observational experiments have been published since 1995. In analyzing these studies, the National Physical Activity Guidelines Report (511) concluded that inactive individuals were ~45% more likely to exhibit depressive symptoms than active individuals. Similarly, 28 prospective cohorts were examined to determine physical activity levels before the appearance of depression symptoms. Physical inactivity for 4 yr augmented the risk of depression by 49%, before any risk factor adjustments. After adjusting for the risk factors of age, alcohol use, chronic health conditions, education, income, race, sex, and smoking, 22% of depression was due to physical inactivity. Furthermore, in eight cohort studies containing the clinical diagnoses of depression symptoms, physically inactive individuals had 40% increased risk of depression diagnosis. Similar trends have been found in children. In children under the age of 15 in the United Kingdom, every hour of exercise reduced depressive symptoms by 8% reduction in depression symptoms (434).

The high association between physical inactivity and depression has made exercise a viable treatment of depression. As early as 1979, Greist et al. (188) found that running reduced depression symptoms similarly to time-limited/unlimited psychotherapy. The need for medication and percentage of relapses were reduced by exercise in depressed patients (11). Strikingly, the depressed patients had greater adherence to physical activity (66%) than to drug medication (40%). An exercise dose of walking roughly 12 miles/wk of walking for 12 wk, consistent with public health guidelines, lowered depressive symptoms by 47% (126). The antidepressive effects of physical activity can be seen as early as after only walking for 30 min/day for 10 days (117).

However, the precise mechanisms by which physical inactivity may cause and/or physical activity may prevent or treat depression remain largely unknown. Decreases in brain neurotransmitters and neurotrophic factors (e.g., dopamine, glutamate, serotonin, norepinephrine, BDNF, endorphins, and endocannabinoids) (209) accompany chronic physical inactivity and provide key hypotheses. Furthermore, many of these relationships have been determined in urine rather than the brain (128), and the precise relationship of physical inactivity with these neurotransmitters has not been studied. These factors are influenced by peripheral factors, which provide more potential explanations by which inactivity causes depression. Agudelo et al. (2) demonstrated that exercise training induces the activation of skeletal muscle PGC-1α1 and kynurenine aminotransferase, an enzyme whose activity is protected from stress-induced increases in depression.

Similarly, after examining cross-sectional studies of greater than 120,000 Americans, the National Physical Activity Guidelines Report (511) concluded that physical inactivity increases the odds for the development of an anxiety disorder. In particular, the National Comorbidity Survey noted that physical inactivity enhanced anxiety disorders by 1.75-fold using raw odds and by 1.38-fold after adjusting for sociodemographic and illness (184). Based on these population-based studies, the National Physical Activity Guidelines Report (511) concluded that moderate (>25 min/day) amounts of exercise (both aerobic and resistance types) lessened anxiety symptoms. Like depression, changes in monoamines and other circulating biomarkers are related to inactivity-induced augmentation in anxiety. Specifically, signaling and production of norepinephrine in the brain stem is reduced with physical inactivity, which is the origination of the only norepinephrine producing neurons serving the cerebellum, hippocampus, frontal cortex, and thalamus (121). Furthermore, chronic wheel running for as little as 30 min/day in rats lessened rises in norepinephrine levels in response to repeated stress (121). However, associations have not been studied among physical activity dosage, setting, and the likelihood of depression. Future studies must examine these relationships to provide additional evidence to support public health recommendations regarding the specific prescription of physical activity required to reduce the risk of depression.

A. The Decline in V̇o_2max With Aging Begins in Early Adulthood in Sedentary Humans: Impact of Aerobic Activity

CRF generally increases until late adolescence or early adulthood and then declines the remainder of life in sedentary humans (9, 116, 321, 430) The lifetime decline in V̇o_2max is not trivial, as Schneider (450) found ~40% decline in healthy males and females (11 independent studies for both sexes) that spanned 20–70 yr of age. However, the percentage declines would have been greater if frailty levels were reached at 16–18 ml O₂/kg body wt. Such a drop to physical frailty values would be 61 and 67% in females and males, respectively, from lifetime highest V̇o_2max values (450). Two important factors contribute to decreases in CRF, biological aging beginning in the third decade of life, and physical inactivity which speeds the decline in V̇o_2max at a given age. Furthermore, cross-sectional studies show the most physically active group has the same V̇o_2max as a three-decade younger, less-physically active group (FIGURE 7).

Aerobic training delays CRF’s decline with aging by two to four decades (FIGURE 7 shows 3 independent data sets). The physiological importance is that this important determinant of healthspan and mortality is not fixed by genes, but is modifiable by the level of lifelong physical activity. Healthspan and life expectancy are lengthened and shortened by aerobic training and physical inactivity, respectively.

In an attempt to better understand and elucidate molecular mechanisms governing the concept of lifetime-apex V̇o_2peak and its initial decline, the Booth laboratory (498) studied rats that had access to voluntary running wheels or were sedentary. The initial hypothesis was that the active rats would experience the decline in CRF later in life compared with the sedentary rats; however, the only benefit that was conferred with activity was the ~20% increase V̇o_2peak noted in the voluntary running group until 6 mo of age. Thus, when rats are sedentary during adolescence, their genetically highest peak CRF is not attenuated. Translating this to humans, if children and adolescents were physically inactive, they would have a lower CRF than their potential maximum.

B. Low CRF Is Associated With Increased Chronic Diseases and Increases Mortality Rate Three- to Fourfold

High levels of CRF are associated with reduced prevalence of several cardiometabolic risk factors including hypertension, hyperlipidemia, inflammation, and insulin resistance and lower incident rates of metabolic syndrome and T2D. Indeed, physical inactivity leads to a decrease in CRF, increasing the risk of numerous chronic diseases/conditions (25, 280, 530). In the Aerobics Center Longitudinal Study (19), the cumulative incidence rate of hypertension was highest in women with low CRF. Each successive 1-MET loss in CRF level was associated with increased hypertension risks of 19, 16, and 32% risk in all subjects, men, and women, respectively.

Mortality begins to increase when CRF falls below ~10 METs (FIGURE 8). Remarkably, when CRF falls from 10 to 4 METs, death rate increases ~4.5 times (4, 44, 256). With regard to the metabolic syndrome, Finnish men in the upper fourth of loadings on the metabolic syndrome factor were 2.3, 3.2, and 3.6 times more likely to die of any cause, cardiovascular disease, and coronary heart disease, respectively (272). In another study of 15,400 healthy men, and 3,700 men with the metabolic syndrome, the middle and lower tertiles for CRF had 2.08 and 3.48 times, respectively, the risk of death from cardiovascular disease than men in the upper tertile (241). The lowest third in V̇o_2max from 676 Finnish women and 671 Finnish men, between 57 and 79 yr old, exhibited 10.8- and 10.2-fold higher risks, respectively, while those in the middle third demonstrated 4.7- and 2.9-fold higher risks, respectively, had the metabolic syndrome as compared with the highest V̇o_2max after performing multivariable adjustments (204). In addition, men with high CRF display significantly lower levels of abdominal adipose tissue compared with those with low CRF (549). Overall, these examples clearly establish that CRF, which is decreased in part through increased physical inactivity, leads to increases in hallmark risk factors for metabolic diseases.

Regarding the impact of CRF on mortality, in the Aerobics Center Longitudinal Study, cardiovascular disease mortality increased 19% for every 1-MET loss in CRF in 14,345 men who were 44 yr old after an average 11.4-yr period (285). Low CRF had a 27% greater risk of cardiovascular disease mortality. Those who exhibited an increase CRF over the 11.4-yr period decreased risk of cardiovascular mortality by 39%.

In addition, a direct dose-response relationship exists between exercise volume (duration × intensity) and CRF (464, 481). Along the same lines, two landmark studies report a dose-response relationship between low fitness and increased mortality. Blair et al. (44) reported two fitness assessments performed on average 8 yr apart on 10,224 men and 3,124 women. From lowest to highest fitness quintile, age-adjusted all-cause mortality decreased 3.4-fold, falling from 64.0/10,000 yr in the least fit quintile to 18.6/10,000 yr in the highest fit quintile. A second report by Myers et al. (349) noted that for every one MET drop in maximal fitness, mortality increased 12%. Two cardiovascular fitness assessments performed on average 6 yr apart on 6,213 men averaging in their 7th decade of life. From lowest to highest fit quintile of cardiovascular fitness, age-adjusted all-cause mortality decreased 4.5-fold among normal subjects and 4.1-fold among patients with cardiovascular or pulmonary disease. Taken together, physical inactivity is a very powerful predictor of mortality once maximal cardiovascular fitness falls to less than ~10 METs (54).

C. CRF Is Not Fixed in Humans: Modifiability Throughout the Lifespan

Trappe et al. (503) mention that two men who had nearly the same CRF (V̇o_2max) at the end of the 22 yr (no aging effect) when they continued the same high volume of endurance training volume (endurance volume is defined as duration × volume). They comment in their discussion (503), “thus it may be possible to augment the decline in aerobic capacity if running training is maintained at a very high level. Although running volume and intensity may drop off slightly, it appears that the reduction in aerobic capacity is significantly less compared with individuals who run less or not at all.” This indicates two points. First, the decline with normal aging between 30 and 50 yr of age could likely be almost 100% due to physical inactivity. An extreme extension of the notion that voluntary physical activity is one driver of the decline in CRF is to observe humans in an old-age facility. Most human lives at the end of a long life are spent sitting and lying down. The behavior of physical inactivity in old age is likely a driver of a negative cycle of inactivity begetting lower CRF (18, 436), which makes voluntary physical activity more fatiguing so less is performed, and so forth in a negative cycle.

In addition, low V̇o_2max can be rescued by adding physical activity to inactive humans. Improvements in risks from co-morbidities and co-mortality are possible with the addition of physical training by inactive humans. A meta-analysis of 160 randomized clinical trials containing 7,487 women and men found that the inactive group did not improve in comparison to the healthy improvements by the exercise-trained group in CRF and in metabolic syndrome biomarkers for lipid and lipoprotein metabolism, glucose intolerance and insulin resistance, systemic inflammation, and hemostasis during the trial (300).

Regarding mortality, in a Cooper Clinic study, 9,777 men aged 20–82 yr old, who changed from unfit to fit over a 4.9-yr period, lowered their mortality risk by 44% (43). Mortality risk was lowered 7.9% for each minute increase in maximal treadmill time between measurements. In a Veterans Administration study of 5,314 male veterans aged 65–92 yr, Kokkinos et al. (255) noted that males who went from a low CRF to a high CRF decreased their risk of death by ~50% over an 8-yr period. The opposite occurred for fit men who became unfit in the two aforementioned studies (255), increasing mortality risk by ~50%. Taken together, physical inactivity can produce the loss of most cardiovascular fitness associated with physical activity.

D. Cardiovascular Fitness Is a Multi-organ Determined Phenotype

Nearly 100 yr ago, Nobel-Prize winner A.V. Hill discovered the concept of maximal oxygen (211, 212). To supply skeletal muscle with the oxygen, atmospheric air must first pass through the lung airways, where oxygen transport into pulmonary capillaries with hemoglobin that will carry 99% of oxygen. While the above may be taken for granted as they are not rate-limiting steps in oxygen flux from air to skeletal muscle mitochondria at sea level, this can become rate-limiting in specific disorders. Maximal cardiac output is thought to be a rate-limiting step (469), while capillarization and diffusion of oxygen to muscle mitochondria are generally not envisioned as rate-limiting in healthy humans. However, the concentration of muscle mitochondria is considered to be a second potential rate-limiting step, to limit V̇o_2max. Even today, we are still trying to understand what limits V̇o_2max (469). For example, Richardson’s group (177) has provided new data on whether maximal cardiac output or skeletal muscle mitochondria are rate-limiting for V̇o_2max. In untrained subjects, V̇o_2max is limited by working muscle’s demand for mitochondrial O₂, with indication of adequate O₂ supply, whereas in trained subjects, the exercise training-induced enlargement in mitochondrial concentration in skeletal muscle causes skeletal muscle V̇o_2max to become limited by O₂ supply. In addition, 7 days of bed rest reduced erythrocyte volume by 9% in healthy men who were 36–40 yr old (97).

E. Peripheral Circulation

One organ that epitomizes physical inactivity’s negative effects is the peripheral vasculature. The deleterious effects of inactivity on the vasculature depend on the type of inactivity and also on the specific region/location of the vasculature (491). For example, regional differences and severity of consequences between resistance and conduit vessels are with physical inactivity and the pathophysiological mechanisms underlying changes that occur are unique to the type of vessel (490). During physical activity and/or exercise, sheer stress and hemodynamic stimuli induce effects on the vasculature, promoting remodeling and an anti-atherogenic phenotype. In contrast, with inactivity and the absence of these physical stresses, endothelial dysfunction and arterial remodeling (186, 491, 528) have been postulated to initiate some of the negative phenotypic manifestations of inactivity on the vasculature.

Vascular responses to physical inactivity depend on the type of inactivity. We will begin with data collected from studies that are less extreme and more physiologically relevant, moving to vasculature consequences resulting from more extreme models of physical inactivity. Boyle et al. (62) used a simple model of reduced step count in recreationally active humans to determine whether reduced physical activity (<5,000 steps/day for 1, 3, and 5 days) altered flow-mediated dilation in the popliteal and brachial arteries. After 5 days, flow-mediated dilation was impaired in the popliteal artery (lower limb), but was not impaired in the brachial artery (upper limb) and increased circulating endothelial microparticles. These findings highlight the vulnerability of reduced physical activity on localized vasculature in a short time period. Along the same lines, a second study from the above research group looked at the impact of prolonged sitting on limb dilator function (417). A 6-h protocol of sitting was performed, and flow-mediated dilation was measured pre-sit, post-sit, and post-walk. The authors found that the impaired dilator function with sitting can be fully reversed after a short 10-min walk in the lower limbs, but not the upper-arm vasculature. In addition, several hindlimb unloading studies have been carried out in rodents examining the effects on the peripheral vasculature. For example, it has been shown that hindlimb unweighting reduces endothelium-dependent vasodilation and expression of endothelial nitric oxide synthase in isolated rat soleus skeletal muscle feed arteries (228, 452). This suggests that with 14 days of disuse, the vasculature’s ability to vasodilate via endothelium-dependent mechanisms is compromised.

Many studies to date examining vascular function in response to inactivity have chosen more extreme models of physical inactivity (39, 45, 46, 112, 198, 352). Bed rest typically does not restrict upper extremity movement; therefore, studying the effects on the vasculature in the lower limbs versus the upper limbs may yield different outcomes. Furthermore, unilateral lower limb immobilization only allows study of one leg and can increase the risk of deep vein thrombosis (33, 47, 170). For example, spinal cord injury patients exhibited increased femoral and carotid artery wall thicknesses after a 6-wk latent period (489). Distinct mechanisms regulate conduit artery wall thickness and diameter both above and below the spinal cord injury. Intriguingly, only the femoral artery diameter (below the spinal cord lesion) decreased over the 24-wk period post-spinal cord injury (489). Thus localized effects occur for arterial diameter (435). Spinal cord injury and 3 wk of unilateral limb immobilization patients (274) both exhibited reduced hyperemic flow, but spinal cord injury was diminished to a greater magnitude. In addition, intima-media thickness-to-lumen ratio was increased with both short- and long-term deconditioning (274).

Transcriptional adaptation to physical inactivity is one mechanistic factor that has been investigated. In the above study, Lammers et al. (274) noted downregulation of transcripts including HIF-2α, which binds the VEGFA and FLT1 promoter regions, VEGF co-receptor NRP, VEGF B, VEGF C, caveolin-1 (CAV1), nitric oxide synthase trafficker, soluble guanyyl cyclase, and the nitric oxide synthase. Several transcripts were upregulated including TGFB1, inhibiting angiogenesis and inducing arterial stiffening, and the authors concluded “thus, the VEGF signaling pathway, regulation through TGFB1 and involvement of extracellular matrix-related proteins seem to be important mechanisms after deconditioning, which may lie at the base of the associated vascular adaptations.”

F. Aerobic Capacities in Hunter-Gatherer Societies

Modern humans age 20–29 and 40–49 yr old have V̇o_2max values of 40 and 35 ml·kg^-1·min^-1, respectively (100). V̇o_2max values of various hunter-gatherer societies exceed those of modern human societies. For example, hunter-gatherer cultures, such as Eskimos living in the Canadian Arctic community of Igloolik, had V̇o_2max values of 49–54 ml·kg^-1·min^-1 (428) and 62 and 42 ml·kg^-1·min^-1 for male and female, respectively, in 20–29 yr old Ache of Eastern Paraguay (526). V̇o_2max values of 60–70 ml·kg^-1·min^-1 are noted in New Guinea Lufas, Mexican Indians, and Tanzanian Masai, and 50–60 ml·kg^-1·min^-1 in Venezuelan Indians and Finnish Laps (100). Cordain et al. (100) interpreted the V̇o_2max values in modern humans as follows: “it should not be surprising that the limited physical activity typical of modern affluent humans generates mediocre aerobic fitness, nor that the aerobic fitness levels of recently-studied foragers are superior to those of Northern Americans.” The above taken together could support the notion that physical inactivity produced V̇o_2max values that are 30% lower than their likely genetic potential.

G. Primary Artificial Selection for Low Endurance Capacity Co-selects for Low Aerobic Capacity

Britton, Koch, and Wisloff developed their working hypothesis: “variation in capacity for oxygen metabolism is the central mechanistic determinant between disease and health (aerobic hypothesis)” (252). As an unbiased test for their aerobic hypothesis, Wisloff et al. (545) employed selective breeding of rats, with the primary selection factor being the distance completed during forced running on the motor-driven treadmill. Two separate lines were artificially bred from the original founder line, with the primary selective factor for generation 1 being either low or high intrinsic endurance running distance (termed “endurance exercise capacity”). Compared with the distance run by the founder population, which was used to breed generation 1, rats selected for the low distance line ran 46% less distance and for the high distance line ran 140% further the distance that the founder runners had run 11 generations earlier. At the 11th generation, the primary selected phenotype (distance during a forced run) had co-selected the two phenotypes (aerobic capacity and risk factors for chronic diseases). V̇o_2max was 58% lower in the low capacity runner (LCR) line and cardiovascular risk factors were worse for LCR, including 13% higher blood pressure, 7.8-fold more acetylcholine infusion required for one-half maximal vascular relaxation, fasting blood insulin and glucose 131% and 20% higher, respectively, lower mitochondrial protein concentrations in skeletal muscle, visceral adiposity/body weight ratio 63% higher, and blood triglycerides and free fatty acids 168% and 94% higher, respectively (545). A follow-up study reported that LCR rats had 28% shorter mean lifespan, with a calculated hazard ratio of 5.7 for death (253). The co-selections of aerobic capacity and chronic disease risk factors in artificial breeding imply some evidence for their co-selection could have occurred in natural selection during evolution and specific genetic differences based on endurance running capacity were developed between LCR and HCR lines. Koch and Britton (250) expressed their view by stating “the strong linkage of disease with low aerobic capacity is consistent with a pivotal role of oxygen in our evolutionary history. Nevertheless, even if these clues about the critical role of oxygen are correct, recognizing the mechanistic footprint of oxygen in our evolutionary path remains a challenge.”

H. Mechanisms

The cardiovascular disease risk factors produced by physical inactivity are mediated via multiple mechanisms. Mora et al. (340) categorized ∼60% of the risk factors for cardiovascular diseases that are found in physically inactive subjects. In rank order from highest to lowest, percentage contributions were inflammatory/hemostatic biomarkers, blood pressure, traditional blood lipids, and novel blood lipids. Remaining physical inactivity-induced risk factors for cardiovascular diseases are not known. A recent review (354) updates the risk factor “gap,” indicating “in fact, epidemiological evidence suggests that the protective effects of physical activity on cardiovascular disease are nearly double that which would be predicted based on changes in traditional risk factors,” suggesting that ~50% of the protection afforded by physical activity remains unexplained. Furthermore, the role of physical inactivity in the “gap” also remains unknown. Joyner and Green (230) suggest that the cardiovascular risk-factor gap producing cardiovascular disease is the attenuation of three positive physiological responses: 1) lower vagal tone so to increase heart rate variability via lessened peripheral baroreflex function and CNS cardiovascular regulation; 2) lower endothelial function so to decrease vascular compliance that diminishes vasodilatation and attenuates peripheral baroreflexes; and 3) heightened baseline sympathetic outflow on blood pressure by diminishing interactions between endothelial function and sympathetic outflow.

An example of different regulatory pathways for cardiovascular adaptations to exercise and deconditioning comes from Thijssen et al. (490), who reported that the vascular responses to deconditioning and exercise did not employ the same pathway in opposite directions. Specifically, they found that the cardioprotective effects of exercise were due to vasodilation by the nitric oxide pathway; however, deconditioning activated vasoconstrictor pathways. Taken together, the above findings provide evidence to support a viewpoint that exercise mechanisms sometimes cannot be simply reversed to explain physical inactivity mechanisms. One outcome from some separate regulatory mechanisms for exercise and inactivity could be that current emphasis on molecular transducers of physical activity as a treatment for modern sedentary chronic diseases/disorders will not reveal with 100% fidelity actual molecular causes of chronic diseases/disorders caused by physical inactivity.

I. “Phenotypic Knock-down” of Cardiovascular Fitness

We define “phenotypic ‘knock-down’” models of physical activity (53) as reductions in physical activity independent of transgenic methodologies. Physiological knockdowns produce physical inactivity relative to the baseline of initial physical activity. Thus “knocking down” physical activity is a primary act of environmental manipulation that causes secondary alterations in gene expression.

Two human models of endurance physical activity knockdown are presented. First, Saltin et al.’s (443) 1968 bed rest found that 20 days of complete bed rest by healthy 20-yr-old men had a 28% decrease in V̇o_2max and a 26% decrease in maximal cardiac output (the latter being produced by a 29% decrease in maximal stroke volume with no change in maximal heart rate) (330). Of further interest is the five subjects exhibited a twofold range in their percentage decrease in V̇o_2max, ranging from 46 to 20% to produce the mean decrease of 28%. After bed rest, the subjects underwent an approximate 8-wk period of endurance training for the aim of returning the subjects to pre-bed rest values, and V̇o_2max increased from 2 to 52% in the retrained subjects. When the same subjects underwent V̇o_2max testing 30 yr later at the age of 50, the percentage decline in V̇o_2max had not decreased as much as it did after 20 days of bed rest when the subjects were 20 yr old. Second, Pedersen’s group (372) had 24-yr-old men reduce their daily step count from 10,501 to 1,344 steps/day for 2 wk. At the end of reduced stepping, plasma insulin increased 57 and 50% during oral-glucose tolerance test and oral-fat tolerance testing (OFTT), respectively, and plasma triglycerides increased 21% more during the OFTT. In addition, intra-abdominal fat mass increased 7% after 2 wk of reduced stepping. Both the Saltin et al. (443) and Olsen et al. (372) studies reported 26 and 7% percentage declines in human V̇o_2max after only 3 and 2 wk of continuous bed rest and reduced daily stepping, respectively. The genes underlying the large decline in V̇o_2max phenotype are currently unknown. In summary, the above two human experiments provide support that large increases in physical inactivity produce remarkably large percentage declines in physiological functions in very short periods.

As sitting is the most common form of inactivity and commonplace in today’s society, it is often reported that this has a significant impact per se, independent of fitness, on chronic disease risk. However, a shortcoming with this research is the lack of control for fitness. Nauman et al. (351) performed a cross-sectional study on 26,400 Norwegian adults and found that high levels of CRF protect against cardiovascular risk factors in men and women sitting >7 h/day, independent of whether or not these subjects were meeting U.S. weekly physical activity recommendations. Remarkably, high CRF (>43.3 ml·kg^-1·min^-1) abolished the odds for increased cardiovascular risk factor clustering (determined on the basis of the definition of the metabolic syndrome) in those subjects with high prolonged sitting times, independent of subjects not meeting current recommendations for physical activity (FIGURE 9). Furthermore, Myers et al. (347) reported that “no deaths were observed among subjects who were both fit (>10 METs) and active (>1,500 kcal/wk)” in their study of 842 males aged 59 yr old,” after subjects had a 5.5-yr follow-up period during which 89 deaths occurred.

A. Overview of Inactivity and Altered Metabolism

Whole body energy metabolism, substrate utilization, and trafficking are altered by numerous factors including nutrition, gender, age, and adiposity. But a largely underappreciated factor altering energy metabolism and substrate utilization is physical inactivity (85, 240). If Darwinian medicine is to be used as a guide, then metabolic pathways evolved under conditions in which physical activity and energy expenditure in most of human existence were performed for basic functions such as obtaining food through hunting and gathering or agriculture work. Such a defense mechanism has been noted in obese states. Rosenbaum and Leibel (432) suggest that physiological mechanisms exist for defense of body fat by acting to override maintenance of the reduced body weight in obese humans (149). To protect against weight loss, such as would occur in a weight loss program, an apparent evolutionary program of persistent hypometabolic and hyperphagic state emerges making it difficult to maintain a new, lower body mass. Their data suggest, “the hypometabolic state is characterized by declines in resting energy expenditure and activity energy expenditure (due to increased contractile efficiency of skeletal muscle), and is augmented by decreased activity of the hypothalamic-pituitary-thyroid axis, decreased sympathetic nervous system tone, and increased parasympathetic tone” (149).

As a part of efficient storage, glucose, which is limited in quantity (532), is utilized in tissues as dictated only by energy demands so as to protect limited circulating reserves of glucose (only 4 g of glucose in circulation and ~300–500 g of glycogen in muscle and liver) (532). Maintenance of glucose levels is essential for brain function and consciousness. The nervous system generally does not oxidize fatty acids except in long-term fasts (73) or in the absence of glucose intake. Fatty acids, which are a quite abundant energy source, are also preferentially shuttled towards energy stores or maintained in stores (adipose depots) if their use is reduced due to lower energy expenditure or constant feeding. However, as physical activity and energy demands increase, these depots of glucose (stored in muscle and liver glycogen) and fatty acids (stored in adipose tissue or within muscle and liver) can be rapidly mobilized to fuel muscle contraction and other essential processes until the work is done and food is procured. Again, Darwinian medicine would indicate that much like a car engine fills up with gas, the motor runs, and the tank must be filled up again, the human body was designed for cyclical periods of energy expenditure and energy storage. However, in the current physically inactive environment this cyclical pattern has theoretically been broken due to physical inactivity and a lack of contractile activity to drive energy demand (85), changes that putatively lay a foundation for subsequent metabolic dysfunction (495). Indeed, accumulating evidence strongly links inactivity to the development of obesity and insulin resistance (240). Although complex, a chronic positive energy balance results in weight gain, expanding adiposity, and obesity. Given that energy balance is dictated by energy intake and energy expenditure, chronic inactivity and associated reduced energy expenditure often leads to weight gain if energy intake levels are not lowered adequately. This concept is illustrated in FIGURE 10.

Adult hunter-gatherers, for example, Tanzanian Hadza, do not hunt, or gather, all day. Rather when not hunting and gathering, energy intake was reduced sufficiently, when inactive to make daily, total energy usage unchanged. Pontzer et al. (401) suggested that their reduced energy observations were an apparent compensation to keep total daily energy usage unchanged. Pontzer et al. (400) recently modified their interpretation of their above data with the constrained total energy expenditure model. The theory suggests that total energy expenditure is maintained at a chronically tight homeostasis even if physical activity levels are changed over a chronic period of time. This is a controversial idea, because it suggests that physical activity or inactivity would not adjust total energy expenditure chronically. Even if true, there is no doubt that the percentage of total energy expenditure comprised of activity energy expenditure would be higher in an active state, and lower in an inactive state. Moreover, energy requirements in tissues and thus cyclical substrate utilization and storage patterns would be quite different between individuals who had the same daily total energy expenditure, but did so with robustly different activity levels within the same day (highly active vs. inactive). Therefore, the flux or lack of flux through these pathways could play a primary mechanistic role in metabolic dysfunction induced by inactivity.

B. Physical Inactivity Produces Rapid Skeletal Muscle Insulin Insensitivity

Although, largely underappreciated, the regulation of skeletal muscle insulin sensitivity (insulin-stimulated glucose transport) is driven by similar concepts as energy balance. Skeletal muscle is a critical glucose disposal site during postprandial conditions, especially if glycogen stores are not full and there is some level of energy demand due to contractile activity. If skeletal muscle is being regularly contracted, ATP demand is elevated, then there is a need for increased glucose to be actively transported into muscle cells (494). This can occur at even higher rates if glycogen is depleted (79, 171). Therefore, higher levels of activity and depletion of glycogen stores promote increased insulin sensitivity. However, if skeletal muscles are inactive, ATP demand is low, and glycogen is elevated, then the demand for glucose is low and thus insulin sensitivity decreases. Indeed, adiposity and physical inactivity contribute more to insulin insensitivity with aging than aging itself (275). Glucose is tightly regulated in this manner putatively because it is a substrate in limited supply [only ~4 g in circulation (532)] and is critical for brain metabolism.

C. Rat Wheel-lock Studies Link Physical Inactivity and Metabolic Changes

As discussed above, Booth and colleagues (267–269) conducted a series of experiments analyzing the effects of transitioning rats from daily wheeling running to cage only (sedentary conditions). Rats were provided with running wheel in their cages for weeks before the wheels being locked, effectively transitioning the rats to physical inactivity. Important aspects of VWR are that rodents run intermittently through the night and display mild improvements in hindlimb muscle mitochondrial adaptations (218, 415), compared with exercise training done on treadmills (154). Therefore, VWR is a model that can arguably emulate bouts of low to moderate intensity ambulatory activity of humans throughout the day. The model allows study of physical inactivity mimicking at least two human conditions, including 1) having an occurrence that temporarily stops daily physical activity, such as an illness, injury, etc.; and 2) a “fast-tracking” the aging portion of life.

Rats typically ran for 3–6 wk followed by being studied acutely after wheel-lock (from a few hours to 7 days post). Daily VWR increased insulin sensitivity in isolated skeletal muscle (267); however, wheel-lock lowered insulin-stimulated glucose transport to the level found in sedentary rats condition within 2 days. In a previous animal study, after a single day of hindlimb immobilization, mouse muscle exhibited decreased insulin responsiveness (457). Additionally, a prior human study observed that only 3 days of absolute bed rest in young healthy men caused significant reductions in peripheral glucose uptake that were similar to 14 days of bed rest, which was secondary to peripheral insulin resistance, rather than insulin deficiency (301). Furthermore, when endurance athletes stopped daily exercise, insulin sensitivity decreased to what was observed in sedentary, age-matched controls in only 2 days (71). Follow-up analysis revealed that reductions in insulin-stimulated glucose uptake into skeletal muscle tracked with reduced insulin-stimulated activation of the insulin-signaling pathway at both the levels of the insulin receptor and Akt, and also tracked reduced insulin binding to the insulin receptor (267). In addition, there was also a reduction in GLUT4 protein content, which tracks with insulin sensitivity levels. In addition, these changes were likely mediated in part by increases in the association of protein tyrosine phosphatase 1B (PTP1B) and protein kinase C (θ) with insulin receptors. PTP1B dephosphorylates the insulin receptor, while protein kinase C is a serine kinase, which putatively blocks tyrosine phosphorylation of the insulin receptor, and insulin receptor substrate, leading to downstream inactivation of the signaling pathway to translocate GLUT4 (444). Certainly, the induction of reduced insulin sensitivity in skeletal muscle after a transition to inactivity would not be considered “pathological.” Thus these changes were physiological adaptations to a condition of reduced energy expenditure within the skeletal muscle, and it appears that molecular mediators that have been implicated in the insulin resistance field played a role in feedback inhibition. However, it can be contended without the aforementioned physiological decrements that the likelihood of progression to the pathology of T2D is lessened. In other words, it would be rare for insulin resistance to develop in skeletal muscle if physical inactivity were eliminated on a daily basis (55).

D. Human Reduced Stepping Studies to Examine Links Between Physical Inactivity and Altered Metabolism

In the aforementioned study, Pedersen and co-workers (263, 372) took young men who were physically active with ~10,500 steps/day and had them change their lifestyle so that they approached 1,400 steps/day for 2 wk. After 2 wk, in response to an oral-glucose tolerance test (OGTT), the plasma insulin in the area under the curve increased 57%, coupled with a 17% decreased in glucose infusion rate during euglycemic clamp, while the area under the curve during an OFTT increased plasma insulin 50% and plasma triglycerides 21%. Additionally, intra-abdominal fat mass increased 7% and total fat-free mass decreased 2%. These studies were validated by studies in the Thyfault laboratory that demonstrated inducing a transition from >10,000 steps a day to <5,000 steps/day after only 3–5 days provided similar results in terms of elevated glucose, and insulin responses to an OGTT and elevated free-living postprandial glucose levels measured by continuous glucose monitors (336, 418). Thus, even with a shorter and smaller change in daily activity levels, insulin sensitivity was lowered. These studies also documented that there were greater swings in the peaks and nadirs of glucose levels measured by continuous glucose monitors worn during free living conditions (336, 418).

Knudsen et al. (249) went on to perform an additional inactivity study using reduced daily steps (from 10,000 to 1,500 steps/day), but also added the effects of hypercaloric burden through subjects eating an extra 1,500 kcal/day during the 2 wk of reducing stepping and found that inactivity, when paired with a 50% elevated energy intake, induced a 50% decrease in insulin sensitivity. A subsequent paper from Pedersen et al. (262) also examined the deleterious effects that a high caloric-intake diet would have over 2 wk on individuals who maintained activity greater than 10,000 steps/day or who reduced activity below 1,500 steps/day. Although both groups gained body mass, the inactive group gained more visceral fat, displayed worse glycemic control, and had greater increases in hepatic glucose production than the 10,000-step group. Stephens et al. (471) examined if 1 day of increased sedentary time would also lower insulin sensitivity and if this depended on energy balance. Subjects either performed a day of increased sitting with their normal caloric intake or a day of sitting in which caloric intake was lowered to appropriately match their lowered energy expenditure, and the authors noted that both conditions lowered insulin sensitivity, but the greatest effect occurred when energy intake was not lowered during the sedentary condition. Healy et al. (207) found interruptions in sedentary time were associated with healthier levels in metabolic risk variables. Increased numbers of breaks in sedentary time were related to smaller waist circumference, lower fasting serum triglycerides, and 2-h plasma glucose in OGTT. The differences were independent of both total sedentary time and moderate-to-vigorous intensity activity time, suggesting that the manner in which total daily sedentary time is accumulated may be important, rather than simply the total accumulated duration of sedentary time.

In addition, a recent study of 2,497 subjects age 40–75 yr old with either T2D, impaired glucose metabolism, or normal glucose metabolism noted that for each additional hour of physical inactivity, the odds of acquiring T2D and metabolic syndrome increased by 22 and 39%, respectively (512). The increased risk of T2D and the metabolic syndrome were independent of high-intensity physical activity. Furthermore, only weak associations with increased metabolic syndrome risks were observed with number of physical activity breaks, number of prolonged physical inactivity bouts, and average physical inactivity duration.

In summary, physical inactivity exerts powerful effects to alter substrate metabolism, including lowered insulin sensitivity, and alterations utilization of fatty acids. These mechanisms provide strong evidence that chronic inactivity plays a fundamental role in metabolic dysfunction and T2D. Without initial, chronic physiological dysfunction, dependent pathological and clinical maladies are less likely to occur (55).

A. Physical Inactivity Contributes to Obesity

As discussed earlier, the Old Order Amish in Canada perform high levels of physical activity, averaging 18,000 and 14,000 steps/day (24). As mentioned, 2,252 individuals aged 13 yr and older had an average of 5,117 steps/day (23), while 325 women (average age 57 yr old) reported 4,944 steps/day (482). Additionally, data from NHANES (2005–2006) noted that 3,725 subjects had an average of 6,549 steps/day from self-reported accelerometers (455), and out of 3,744 adults, 84% were below 10,000 steps/day (36.1% had <5000 steps/day) as determined by accelerometers (463). Taken together, daily step numbers are less than half in the general population as compared with Amish adult men and women, equivalent to at least 300 kcal/day on average (>7,500 step difference ÷ 2,500 steps/100 kcal/mile). As body fat is largely a balance of fat calories expended and fat/sugar intake, it is also of note that Amish males report a greater energy intake (2,780 kcal) compared with non-Amish males (2,298 kcal) (107). In another study, average daily caloric intake for non-Amish men and women was 2,504 and 1,771 kcal, respectively (550). Together, the data suggest caloric intakes of Amish are greater than non-Amish. Some might suggest that Amish should then have higher percentages of body fat. However, Amish have very low levels of obesity, with 0% of the men and 9% of the women having a BMI ≥30 (24), compared with 35% in men and 40% in women adults (155), and 1.4% of the Amish youth being obese (22), compared with 17% of U.S. children (369). Taken together, both physical inactivity and caloric intake play shared roles in obesity.

Four other historical studies are of interest. One report contains BMI values of U.S. adults from 1882 to 1986 (257). The rise in U.S. BMI values peaked in the early 1920s, declining to a trough in 1945, and beginning to increase again by 1950. The declining period of BMI includes the years of the great depression (1929–1941) and World War II (1941–1945). Another comparison statistic is the number of automobiles. U.S. automobile number (in millions) were 8, 17, 23, 23, 27, 26, and 40 in the years 1920, 1925, 1930, 1935, 1940, 1945, and 1950 (368). Automobile numbers were relatively flat from 1930 to 1945. An association thus exists between percentage decline in BMI and an essential plateau in automobile number. A second study by the pioneer Jean Mayer concluded that within the sedentary activity range, decreasing physical activity was not associated with a decrease in food intake, but paradoxically was associated with an increase in food and body weight in mill workers in West Bengal (327). A third study covered the years following the 1991–1995 Cuban economic crisis. From 1995 to 2010, bicycling decreased from 80 to 55% of the total population concurrent with caloric intake increasing from 2,400 to 3,200 kcal (159). During this time, obesity rose 58%, from 33.5% in 1995 to 52.9% in 2010. A fourth paper reporting on 155,000 United Kingdom women and men reported higher BMI in car-only individuals when compared with mixed public and active transport commuters (156). Therefore, we caution as to whether physical inactivity or caloric intake is more important relative to the other in human obesity. We suggest that both have a participating role in weight gain, with each’s relative percentage dependent on the situation.

B. Overview for Current Physiological Regulation of Adiposity

Basal metabolic rate (BMR) is defined here as the minimal rate of energy expenditure needed to keep the human body alive with all of its vital functions at rest. While various BMR values occur for differences in age, gender, lean body mass, and body fat, among other factors. The storage of glucose is limited with the amount of glucose in blood and stored as glycogen on the same order of magnitude (532) as the daily BMR. Thus, to conserve glucose as a source of calories, fatty acids are important alternative sources of ATP. Importantly, neurons and erythrocytes normally oxidize glucose, although neurons can adapt to be able to oxidize fatty acids. Fat is very efficient per unit of its mass in storage of calories, as exemplified by a 200-pound human with 20% body fat approximating 140,000 stored fat calories, and storage of triglycerides is favored in adipose tissue energy to be available to substitute when food was not available. In the past, evolutionary pressures often limited fat stores, and Reed et al. (416) noted, “based on a dataset of 1,977 knockout strains, we found that that 31% of viable knockout mouse strains weighed less and an additional 3% weighed more than did controls.” Thus ~10 times greater molecules contribute to store triglycerides than to oxidize them.

C. Both Childhood Obesity and Inactivity Have Increased Dramatically in the Past Half Century: Links to Adult Obesity

The likely causes of childhood obesity include environmental changes by diet, physical inactivity, and/or epigenetic changes that induce changes in gene expression, versus new DNA nucleotide sequences. Obesity is defined as a BMI value at or above the 95th percentile for children. From the period of 1971–74 to 2013–14, the percentage of U.S. children (2–19 yr of age) who are “obese” increased from 5.1 to 17.0% (369, 370). Interestingly, from 2003-04 to 2013–14, obesity in youth did not change. These changes are associated with a large increase in physical inactivity. The mean minutes of moderate to vigorous physical activity per day (FIGURE 6) showed continuous yearly drops of ~67 and ~60% when the activity was plotted from 6 to 11 yr old, and then essentially leveling off each year from 12 to 19 yr of age (547). It seems reasonable to propose the notion that the 60% drop in physical activity from 6 to 11 yr old would increase body adiposity as well as numerous other cardiometabolic risk factors. The data beg the question of what the percentage of obese children have been if moderate to vigorous physical activity were to have been maintained at the 6-yr-old level until 11 yr of age? Also, what is the biological basis for the 60–67% drop in moderate to vigorous physical activity per day? The Wolff-Hughes et al. (547) data suggest that although other factors definitely contribute, this decrease in activity likely contributes to the increase in childhood obesity. Of course, a limitation is the lack of direct measures of calorie expenditure; however, while decreased physical activity does not provide absolute caloric values, the magnitude (60 and 67%) in the percentage decline in daily duration of moderate physical activity implies decreases in caloric expenditures in children.

A recent study (270) used accelerometers to estimate moderate to vigorous physical activity levels and DXA to determine body composition between 1998 and 2014. Only 9.5% of the “consistent active” subjects had entered the “becoming obese” group at age 19 yr old, while 23.8% of participants in the “decreasing active” group entered the “becoming obese group” and 88.3% in the “consistently inactive” or “decreasing activity” groups were in the “consistently obese” group. The authors (270) summarized that “adiposity development at age 13 or younger could be critical to determining obesity development in young adulthood. This finding sheds light on the importance of adiposity development during prepuberty and puberty, and supports current public health efforts to prevent obesity by focusing on early childhood.” Intriguingly, the lifetime peak of voluntary running in wheels by female rats discussed earlier occurs around the age of puberty (498).

A recent meta-analysis by Simmonds et al. (462) of the clinical importance of childhood obesity in increasing the odds of adult obesity was performed that included large prospective cohort studies. Overall, obese children and adolescents had 5.2 times the probability of becoming obese adults as compared with nonobese children and adolescents, ~55% of obese children retain being obese in adolescence, ~80% of obese adolescents maintain having obesity when in their 20s, and ~70% will be still be obese over age 30. The authors cautioned that ~70% of obese adults were not obese as children or adolescents, so what contributes to obesity in childhood should also be targeted in adults.

D. Physical Inactivity Causes Obesity

Importantly, the medical community largely believes that obesity, and particularly central obesity (abdominal or visceral) (129, 162), is a primary cause of insulin resistance; thus the role of physical inactivity can be largely ignored. However, an adipocentric hypothesis is not correct. The putative adipocentric mechanism is that obesity and in particular expansion of visceral adipose leads to increases in circulating fatty acids and inflammatory cytokines, which promote insulin resistance (460, 447). Insulin resistance, the reduced ability of insulin to promote glucose transport, is functionally simply “reduced insulin sensitivity” and is often used in terms of describing a pathological condition, although there is no clearly established clinical definition of insulin resistance. Indeed, experimental studies in humans, rodents, and muscle cells all support the hypothesis linking visceral adiposity and increased circulating factors that promote insulin resistance in skeletal muscle (34, 309). Free fatty acids and inflammatory cytokines such as tumor necrosis factor (TNF)-α reduce insulin sensitivity in all of these models (309, 444). However, the common feature in these studies may be inactivity or reduced energy expenditure that must likely be present for cytokines or free fatty acids to decrease insulin sensitivity. In other words, this only occurs when skeletal muscle energy demand is low, such as is found in physically inactive humans living in a modern environment or rodents in cages without access to running wheels.

Importantly, one opposition to the adipocentric hypothesis is that humans who are overfed calories or rodents that are hyperphagic or fed high-fat diets do not develop insulin resistance if they are active or exercising (190, 261, 249, 415, 536). For example, Haskell-Luevano et al. (203) found that voluntary running of melanocortin-4-receptor null mice prevented obesity and diabetic metabolic syndrome from developing, as had been reported in sedentary, melanocortin-4-receptor null mice.

The removal of wheel-running allows a rapid initial growth in adipose tissue mass following its retarded growth by wheel running in rats just post-weaning when they are growing (267, 281, 282, 439) or examining adipose tissue regrowth after a dietary restriction (477). Rats that had been exposed to VWR had reduced adiposity, body fat, and body weight compared with sedentary rats that did not have access to wheels. However, after only 2–7 days of wheel-lock, when rats were no longer undergoing voluntary running, total mass of various abdominal fat pad depots (epididymal, retroperitoneal, perirenal) and total body fat (measured by DXA) increased dramatically and matched levels found in sedentary animals (268, 282). Thus a transition to inactivity for only a few days caused an accrual of abdominal fat that occurred over multiple weeks in sedentary rats. Further analysis revealed that both the cell volume and lipid content of adipocytes within epididymal fat depots also increased following wheel-lock. In addition, Kump et al. (268) found that palmitate incorporation into triacylglycerol of adipocyte homogenates, an in vitro assessment of triacylglycerol synthesis, increased by ~3.5-fold from 5 h of wheel-lock compared with only 10 h of wheel-lock. Furthermore, palmitate incorporation into adipocyte triacylglycerol at 10 h after wheel-lock no longer cycled, but progressively continued to increase for 24 h/day at 1 and 2 days following wheel-lock, an effect described as an overshoot due to wheel lock-induced inactivity (53). These animal findings resemble a body of human evidence that physiological mechanisms exist to defend adiposity after weight loss programs (149). Furthermore, VWR rats consumed more food on a daily basis than age-matched, sedentary rats, and they continued to consume more food at a decreasing daily amount for 3 days following wheel-lock before matching food intake in age-matched, sedentary rats never exposed to VWR (282). Therefore, logic would indicate that the increased food consumption and associated positive energy balance might be driving the rapid increases in adiposity found during wheel-lock. However, Booth and colleagues (282) performed followup studies in which wheel-locked rats had food intake controlled by pair feeding to adjust food intake to what it should be in their new physically inactive state, and a rapid increase in abdominal adiposity and body fat after 7 days of wheel lock still occurred. The group went on to use transcriptomics analysis in the perirenal adipose tissue and detected an enrichment of transcripts having functions for proinflammation, extracellular matrix, macrophage infiltration, and immunity (439). Unloading of the rat hindlimbs had a delay of 4 h before a rapid fall in heparin-releasable lipoprotein lipase activity from its soleus muscle so that by the 10th hour of unloading, 95% of heparin-releasable lipoprotein lipase was no longer present (36).

Furthermore, in humans, daily physical activity is highly correlated to insulin sensitivity, and this is only modestly attenuated by adiposity (16). Therefore, inactivity generally must be present for insulin resistance to occur. Indeed, inactivity is a key etiological factor in the development of insulin resistance through two mechanisms: 1) inactivity, itself, lowers insulin sensitivity, and 2) inactivity provides a permissive environment whereby signaling molecules can impair insulin signaling processes and further reduce insulin sensitivity. Interestingly, the transition of rodents and humans from high to low levels of daily activity as a model to study inactivity leads to dampened insulin sensitivity and increased central adiposity that occur at the same time frame. Thus not only does inactivity promote insulin resistance through reduced utilization of glucose in muscle, but it may also promote the increased storage of glucose and free fatty acids into adipose tissue. Inactivity-induced reductions in fuel utilization of both glucose and fatty acids may divert these fuels to storage in adipose tissue. As such, adiposity and insulin resistance may develop at the same time through these processes. In addition, impaired insulin signaling was not found in muscle cells treated with TNF-α if they were contracted (273). In summary, the findings in this section, when taken together, suggest that acute changes in physical inactivity induce robust and rapid changes in metabolism.

A. Muscle Mass and Function Is Impacted by Activity and Inactivity Throughout Life

This section continues theme 5 that continuous physical inactivity accelerates the lifelong decline in skeletal muscle mass and functional ability for strength throughout life, from childhood to the elderly. For example, in youth, Behringer et al. (29) reported in a meta-analysis a progressive strength with age and increase for trainability of muscular strength age. Twin studies noted that consistently inactive twins had 20% lower knee extension forces than their active twins, and mid-thigh muscle cross-sectional area was 4% smaller in the inactive twin (294). In the aforementioned study from Krogh-Madsen et al. (263), reduced daily step counts led to reduced leg lean mass, a decrease in peripheral insulin sensitivity, as well as the insulin-stimulated ratio of pAkt^thr308/total Akt in the vastus lateralis muscle. In older subjects (mean age 72 yr), Breen et al. (65) produced a 76% reduction in daily step number to 1,413 steps/day for 14 days, and leg lean mass was reduced 3.9%. In the postprandial state, protein synthesis rates were attenuated 26%, and insulin sensitivity decreased 43%. Thus skeletal muscle is one of the most vital tissues impacted by the effects of physical inactivity (150).

B. Protein Synthesis Rates of Skeletal Muscle During Physical Inactivity: Links to Adaptive Remodeling

Early animal models demonstrated the importance of the effects of inactivity on protein synthesis. For example, in 1977 Goldspink (181) noted that protein synthesis in incubated soleus muscle decreased ∼20% after its removal from the rat at the sixth hour of the muscle’s immobilization in a shortened position. The Booth laboratory (57) noted that the fractional rate of soleus protein synthesis was decreased 37% in the first 6 h of hindlimb immobilization, which had the muscle fixed in a shortened position. The rapid changes in the onset of decreased protein synthesis of rat skeletal muscle are not unprecedented when considering hypertrophy studies in adult fowl (278). Thomason and Booth (493) showed that skeletal muscle protein synthesis significantly declined in the first 5 h of non-weight bearing (492), and then remained suppressed. You et al. (553) provide data that hindlimb-immobilization-induced muscle atrophy was associated with decreased protein synthesis independent of mTOR activity and cap-dependent-translation, as mTOR signaling was elevated, rather than suppressed. These earlier animal studies later led to confirmation in human studies. Phillips et al. (396) concluded, “it is now generally agreed that in humans, protein synthesis is downregulated (in skeletal muscle) as a result of uncomplicated (i.e., nonpathological) disuse.” Studies these authors cited reported human leg immobilization and included decreased protein synthesis rates of quadriceps muscles of 27, 25, and 23% after 14 days (179), 37 days (175), and 42 days (176), respectively. An additional study (148) found a 50% decrease in vastus lateralis muscle of men after 14 days of bed rest. Wall et al. (527) suggested that muscle protein synthesis rates are suppressed by 2–4 days in human leg models of physical inactivity and that they remained suppressed.

Often, increases in protein degradation are transient and related to adaptive remodeling to a new smaller size. Because valid methodologies for direct measurements of protein degradation are lacking, determination of protein degradation rates are made by indirect estimations from direct measurements in rates for muscle protein loss and changes in muscle protein synthesis rates. A transient rise in protein degradation would be consistent with Phillips et al. (396): “proposing that most of the loss of muscle mass during disuse atrophy can be accounted for by a depression in the rate of protein synthesis,” as a transient increase in degradation would be a minor percentage of total protein lost. Bodine and Baehr (49) noted: “animal and human data suggest that under conditions of disuse, MuRF1 and MAFbx RNA expression rapidly increases for a relatively short period of time, and thus the inability to measure elevated levels of MuRF1 and MAFbx after extended periods of unloading should not be interpreted to mean that these genes have not had a significant impact on the atrophy process.” Five days of unloading of rat soleus produced an increase in transcriptional activation of MuRF1, suggesting that NF-κB sites, not FoxO sites, were required for MuRF1 promoter activation in the unloaded soleus muscle (551). Baehr et al. (12) later observed defects in the proteostasis network in old skeletal muscles contributed to defects in remodeling of muscle fibers and functional recoveries of muscle mass and strength.

Goldberg and co-authors (397) provide a summary of comparative biology for skeletal muscle atrophy and hypertrophy among the diverse species of Drosophila, rodents, and humans. They indicate shortcomings of Drosophila for translational studies of exercise/inactivity are lack of satellite cells and expression of ubiquitin ligase MuRF, skeletal muscle growth only occurs during development, and no muscle atrophy was seen in flightless Drosophila mutants.

C. Sarcopenia

Rosenberg (433) proposed the term sarcopenia after the Greek sarx (514) and penia (loss) in 1988. Five years later, Evans and Campbell (140) defined sarcopenia as “the age-related loss in skeletal muscle mass, which results in decreased strength and aerobic capacity and thus functional capacity.” However, the consensus definition of sarcopenia is currently unsettled: “although sarcopenia has been researched for many years, currently there is a lack of consensus on its definition. Some studies define sarcopenia as low muscle mass alone, whereas other studies have recently combined low muscle mass, strength and physical performance suggested by the European Working Group on Sarcopenia in Older People, as well as the Asian Working Group for Sarcopenia. The arbitrary use of various available sarcopenia definitions within the literature can cause discrepancies in the prevalence and associated risk factors” (244). This is based on data to suggest that low skeletal muscle strength, rather than low mass, may be a better predictor of mortality (355). Grip strength, although it has limitations, is a simple measure of overall muscle strength (421), onset of sarcopenia (105), future disability (411, 420), physical health problems (99), cognitive decline (420), and both morbidity and mortality risk (292). The definition is evolving (105, 138, 152, 215, 317) and is likely to continue. McGregor et al. (329) suggested that “there is now evidence that not only changes in muscle mass but other factors underpinning muscle quality including composition, metabolism, aerobic capacity, insulin resistance, fat infiltration, fibrosis, and neural activation may also play a role in the decline in muscle function and impaired mobility associated with ageing.” Nevertheless, sarcopenia is a multifactorial geriatric syndrome that is affected by the endocrine system, growth factors, muscle protein turnover, behavior-mediated pathways, and inflammatory-mediated pathways and redox-related factors (106).

With the added years of life expectancy in the 20th century, a major clinical significance for a large number of individuals is the loss of mobility. The near elimination of most communicable diseases in the last century increased life expectancy by decades, but also increased chronic diseases like sarcopenia. The newly added physical frailty with increased life expectancy has increased the probability of falls, as well as an impaired ability to independently perform activities of daily living. Approximately 95% of hip fractures occur after falling (27).

This ultimately results in a lower quality of life in the extended end years of life (139). However, the origins of sarcopenia are complex. Nair (350) describes physical inactivity as inherent with aging. Additionally, aging is environmentally multifactorial in progression, and physical inactivity is only one of many environmental factors contributing to sarcopenia. Since chronic diseases are related to skeletal muscle mass, a delay in development of physical activity would slow aging when chronic disease occurs and when physical frailty first becomes clinically present.

Sarcopenia and inactivity amplify each other in a negative cycle leading to physical frailty. Fried et al. (163) proposed the concept of a negative cycle of frailty, which may begin with sarcopenia begetting lower strength and power, that, in turn, lowers walking speed, leading to lower levels of physical activity (inactivity), which then speeds sarcopenia. The cycle is continuous over the remaining period of life, unless intervened to slow down by resistance training. Thus sarcopenia and physical inactivity both lead to weaker skeletal muscle. Wolfe (546) noted: “elderly individuals, particularly women, are often too weak to perform the intensity of exercise necessary to induce the same magnitude of physiologic adaptations that occur in younger subjects.” Breen et al. (65) point out that older adults do not recover muscle mass and strength, even after heavy resistance exercise, as compared with younger individuals (220, 476). They also indicated that the failure to recover lost skeletal muscle because of inactivity would add to the progression of sarcopenia (64, 65), in agreement with animal data (540). That being said, a meta-analysis of resistance-trained subjects ≥50 yr of age reported percentage increases in strength of 29, 33, 24, and 25% for leg press, knee extension, chest press, and lat pulldown, respectively (394). In 1994, Fiatarone et al. (151) studied elderly men and women and noted that in trained subjects (combined groups with or without supplementation), muscle strength, gait velocity, stair climbing power increased by 113, 11, and 28%, respectively, but thigh muscle cross-sectional area increased only 3%. In another study, elderly males (mean age 82) gained strength, but not enlargement of fiber diameter with resistance training, as compared in an age-matched inactive group (465). The same trend occurred in elderly females (mean age 82), exhibiting a 26% increase in strength, but no increase in thigh muscle cross-sectional area and fiber diameter after 12 wk of resistance training (412). In contrast, resistance training in 74-yr-old men produced both increased strength and fiber diameter (502). In older adults, even endurance training may help preserve skeletal muscle mass. Men (202) and women (201) in their 70s who performed 12 wk of cycle-ergometer training had modest increases in quadriceps muscle volume and knee extensor power increased 20% (men) and 55% (women). We speculate that these subjects had so little physical activity that even endurance training was effective in producing muscle enlargement, which is usually not observed in endurance training studies of young adults. Regardless, Wolfe contends that “rather than initiate practices to reverse sarcopenia, it would be more effective to prevent its development” (546). He argues the need to intervene against sarcopenia should begin at middle age rather than later in life. Ortega et al. (374) noted that deaths from all causes, suicide, and cardiovascular diseases were 41, 70, and 46% higher, respectively, in the 1.1 million Swedish males with the lowest strength level 24 yr after a determination of their initial muscle strength at 16–19 yr olds. Nose’s group (322) found a 95% adherence to a 5-mo training program of interval walking training program produced a 15% increase in V̇o_2peak and 20% decrease in lifestyle-related disease risk factors in 696 older men and women.

Lexall et al.’s 1988 paper (295) reported cross-sectionally that sarcopenia in men begins in as early as the third decade of their life, with acceleration after 50 yr of age to annual decreases in skeletal muscle mass rate of 1–2% and in muscle strength of 1.5% (522). Strength losses accelerate to ~3% a year after age 60. Sarcopenia was associated with a loss in total fiber number. Drey et al. (124) extended Lexall’s findings to include female and male Master’s athletes over the age of 65 yr old, who performed power-lifting. They had attenuated loss of muscle mass and motor units as compared with physically inactive and endurance-trained subjects of the same age.

The molecular mechanisms of sarcopenia caused by physical inactivity are not entirely clear, as suggested by Bowen et al. (60) who stated “the molecular mechanisms underlying how exercise prevents age-related loss of muscle mass are still poorly understood.” Extending this, even less is known regarding the molecular mechanisms by which physical inactivity causes the age-related loss of muscle mass. Two possible modes for physical inactivity accelerating sarcopenic progression exist, including a direct interaction with inherent aging genes that produce mechanisms that cause sarcopenia, and independent actions, such as physical inactivity, that modulate the rate of sarcopenia (FIGURE 11). However, lifelong resistance training by elite Master’s athletes can slow the absolute amount of muscle mass lost at a given chronological age with aging (384). Inherent genes for physical inactivity (498) and for aging (78) contribute to the loss of functional reserve by the decline of total body mass of skeletal muscle. Amino acids represent nutrient regulators of skeletal muscle anabolism, capable of enhancing lean mass accrual with resistance exercise, and also attenuating the loss of lean mass during periods of energy deficit, although factors such as protein dose, protein source, and timing of intake are likely important in mediating these effects (89). Glower et al. (179) concluded that much of the atrophy is due to a drop in postabsorptive protein synthesis rate caused by “anabolic resistance” to amino acids. An increased perfusion of amino acids diminished the reduction, but rates did not totally recover pre-atrophy rates. Furthermore, Phillips (395) suggests that clinical research is needed to test nutritional and supplement-based strategies with resistance training to prevent sarcopenia.

D. Myokines

The term myokine was first defined by Pedersen et al. (386) as, “cytokines and other peptides that are produced, expressed, and released by muscle fibers and exert either paracrine or endocrine effects.” This provided the first evidence of skeletal muscle-secreting molecules having functional effects at a distant site from their origin of secretion, proving that skeletal muscle has an endocrine function. Interestingly, Goldstein (182) documented the idea nearly 60 yr ago. His working hypothesis in a 1961 Diabetes editorial (182) was that skeletal muscle fibers possessed a “humoral” factor that skeletal muscle contraction caused to be released into the circulation to be a message that muscle was consuming glucose to fuel the work. He experimentally had tested his hypothesis by transfer of the putative “humoral” factor by means of a cross-transfusion of both blood thoracic lymphatic fluid from the effluents from contracting skeletal muscle from one exercising animal into a second resting animal, which became hypoglycemic. Goldstein (182) noted that “with strenuous induced exercise one can enrich the body fluids of an animal with hypoglycemic properties which can then be transferred to a resting preparation without other concomitants of exercise such as pH shifts, hyperpnea, circulatory changes, etc.”

Pedersen and Goetz (389) detailed the intermittent history that led to the knowledge that skeletal muscle is an endocrine organ that influences metabolism in virtually all organs in the body (30, 451). One study design included transgenic mice that had GLUT4 overexpression of seven- and threefold in fast-twitch muscle fibers and heart, respectively, resulting in higher insulin-stimulated 2-deoxyglucose uptake and glycogen concentration (507). Remarkably, voluntarily running distance in wheels was four times greater in GLUT-4 overexpression mice than in wild-type mice (508), suggesting the potential for brain regulatory sites for voluntary running being at a “distant site” that had the ability to “sense” some unknown signal from the metabolic status of skeletal muscle.

Pedersen et al. (391) proposed that IL-6 met the earlier Goldstein criteria of: 1) produced and secreted from a tissue into the bloodstream in response to muscle demand for glucose to fuel its contractions and 2) exerted its effects at “distant” organs from the contracting skeletal muscle. They noted that the IL-6 gene is silent in resting skeletal muscle and is activated rapidly by muscle contractions with marked increases in muscle IL-6 mRNA. This IL-6 peptide is released in high amounts into the circulation from contracting skeletal muscle and exerts its effects at distance sites from the muscle, such as adipose tissue, where it induces lipolysis and gene transcription in abdominal subcutaneous fat, increasing whole body lipid oxidation (391). However, this concept (387) was not met with immediate approval because it came after the dogma that a continuous low level of increase in IL-6 is “pro-inflammatory.” Exercise-induced IL-6 produces a large transient rise in IL-6 which exerts anti-inflammatory effects. Pedersen and Febbrario (388) stated that “myokines may mediate protective effects of muscular exercise, with regard to diseases associated with physically inactive lifestyle.” In physically inactive humans, chronically low increases from basal plasma levels of IL-6 exist and closely associate with the metabolic syndrome (388). IL-6 was suggested to have systemic effects on liver and immune system, as well as to play a role in crosstalk between intestinal L cells and pancreatic islets. Skeletal muscle is now considered an endocrine organ (153, 239, 290, 403, 429). Taken together, acute exercise of sufficient intensity produces a large percentage rise in IL-6 that is transient, and importantly has an anti-inflammatory effect, while chronic physical inactivity produces a continuous, low-level increase in IL-6 that has pro-inflammatory effects (238). Additionally, Schnyder and Handschin (451) commented concerning myokines that, “What is missing is the response of the skeletal muscle system to physical inactivity. So far, only myostatin or ciliary neurotrophic factor (CNTF) might fit that description; however, it would be surprising if myostatin and CNTF were the only inactivity myokines. Overall, it has long been known that physical activity produces changes in levels of hormones from classical endocrine organs (169, 478, 487), and it is likely that skeletal muscle as a gene regulatory endocrine organ will help our understanding of the interaction of the role of skeletal muscle with inactivity-related chronic disease (239).

In addition, the release of IL-6 from contracting skeletal muscle induces the production of bioactive osteocalcin in bone, allowing its release into blood. A feedforward loop is formed as osteocalcin, in turn, further increases IL-6 release (56, 333). In contracting skeletal muscle, osteocalcin increases glycogen breakdown to glucose, GLUT4 translocation to the sarcolemma that increases glucose uptake into muscle, and fatty acids uptake and catabolism.

E. Signaling Mechanisms for Physical Inactivity to Produce Atrophy and for Physical Activity to Produce Hypertrophy Are Not Simply Reversal of the Same Pathways

Our notion is that the mechanistic continuum for physical activity to inactivity is not the same as going from inactivity to activity. For example, the mechanisms underlying muscle atrophy to muscle growth are not simply the reverse of a growth state to atrophy. The same could be said for 1,500 to 10,000 steps/day, compared with the reverse. Rather, separate cellular mechanisms for physical activity (82a) and physical inactivity (actual cause) often exist. Regarding the first example, with simple logic, one might assume that hypertrophy is simply the result of protein synthesis, while atrophy is exclusively due to protein degradation. However, data support the concept that protein degradation increases in both skeletal muscle hypertrophy and atrophy. Millward et al. (278) estimated that protein degradation rates of skeletal muscle increased during hypertrophy of chicken skeletal muscle. The unexpected increase in protein degradation was deduced from the increase in the rate of protein synthesis which exceeded the directly measured enlargement of skeletal muscle mass. They suggested that the protein synthesis rate was greater than needed because nascent proteins made were in excess of protein incorporation into muscle. They proposed that nascent protein not assembled into structure was rapidly degraded, termed “wastage” protein synthesis. Thus protein degradation of skeletal muscle is not only increased in atrophy, but also increases during hypertrophy. Stein and Bolster (470) provided more evidence for this concept and compared skeletal muscle transcripts between two treatments, one for muscle atrophy [Lecker and co-workers. (284, 470)] versus the other for muscle regrowth from atrophy [Fluck et al. (157)], and concluded “comparison of these two gene lists for atrophy and hypertrophy showed virtually no overlap. This is a common finding in biochemistry. Anabolic and catabolic pathways are usually separate.” This section, taken together, illustrates theme 1 on the dissociation of pathway reversibility for activity and inactivity.

A. Physical Inactivity Prevents Optimal Bone Maturation Early in Life: Relation to Lifetime Bone Mass Apex

Gunter et al. (193) provide a current viewpoint on the state of current information for bone health in children: “. . . Based on our work (191, 192, 194, 225–227) and that of others (361, 445), we hypothesize that engaging in regular and well-designed targeted physical activity in childhood is crucial to maintaining a healthy skeleton in adulthood. In fact, considering that 60% of the risk of developing osteoporosis can be explained by the amount of bone mass accrued by early adulthood (26), physical activity undertaken during or prior to puberty may have greater positive effects on bone mass than many pharmacological interventions undertaken by adults with osteoporosis.”

During growth and developmental years, nonphysically active youth will end up having 10–20% less peak bone mineral density compared with youth undergoing regular weight-loading physical activity during the growth phase (14, 21, 28, 195, 235, 236, 305, 499). Numerous studies document physically inactive adolescents not gaining various indexes of bone health, as compared with groups undergoing weight-bearing activities on bones. For example, less active 4- to 7-yr-old boys and girls had accelerometer-determined lifestyle physical activity levels, as well as proximal femoral measures of the neck, intertrochanteric, and shaft cross-sectional area (315) and section modulus (Z), indexes of axial and bending strength, and noted that boys and girls in the top third of vigorous activity had 7–9% higher CSA and 9–12% higher Z than the bottom third (224). Another study investigated 8-yr-old children that underwent 7 mo of non-weight-bearing (skeletal muscle stretching) or weight-bearing (jumping) physical training. Children in the stretching program had 4% less bone mineral content at their hip than those children who completed high-impact jumping exercises, and 8 yr later, when 16 yr old, the subjects in the stretching group had 1.4% less bone mineral content at their hip than the jumping group (191). A meta-analysis of 17 studies found that pre-pubertal non-gymnasts had smaller distal radius bone mass density and bone mineral content than pre-pubertal gymnasts (72). In addition, in 6- to 10-yr-old children, a non-jumping group had 4.5 and 3.1% lower femoral neck and lumbar spine bone mineral content, respectively, than high-intensity jumping group after 7 mo (168). Taken together, less physical activity during childhood leads to less bone strength during maturation. We speculate that those who enter adulthood with weaker bone strength have a greater probability of more bone fractures later on in life. Extending inactivity comparisons to young adults showed similar trends as the childhood data. For example, males (29–30 yr old) who were more physically inactive than when first measured between 8 and 15 yr of age had 13% lower adjusted torsional bone strength and 10% less adjusted tibial diaphysis density (125); females responded similarly. Thus inactive adolescents begin adulthood with a deficit in bone health. Others have speculated that optimizing peak bone mass in early life could be helpful to delaying osteoporosis later in life (28).

The lifetime apex for bone mass (i.e., peak bone mass) occurs at ~18–20 yr of age (26). By age 18, at least 90% of peak bone mass had been acquired, with the remaining 10% to be added later in the skeletal consolidation phase (14). The clinical importance of obtaining the genetically highest possible bone mass in youth is that it is difficult to add bone mass above that lifetime apex value after the second decade of life.

B. Bone Health After the Lifetime Apex

Bone mineral density is maintained at a maximum until ~30 yr of age, with loss of bone mineral thereafter (69). Bailey et al. (14) suggested the amount of bone mineral that is laid down in the 2-yr peak bone mass content velocity period during adolescent years is similar to the amount that most individuals end up losing during their later active lives (13). The Tromsø Study, a cross-sectional study (544), consisted of 7,273 subjects aged 24–84 yr old. The 80-yr-old, smoking, physically inactive subjects of both sexes were estimated to lose 25 and 38% more bone mineral density at female’s distal and ultradistal sites, respectively, and 39% more bone mineral density at male’s forearm sites.

C. Bone Loss in the Absence of Gravity

Some types of physical inactivity causing weak bones include skeletal muscle denervation/paralysis, space flight, bed rest, and aging (468). Hindlimb unloading inhibits bone formation, while bone resorption is enhanced or unchanged (258). In humans, spaceflight led to 20-fold greater bone loss in space than on Earth (518), at 1.8–2.0% bone loss per month. In addition, bones of astronauts and cosmonauts lost 11% (range 0–24%) of their total hip bone mass over the course of 4–6 mo in Skylab (518). Aerobic exercise in space did not inhibit head bone loss. Further support that endurance inactivity is not the primary factor for accelerated bone mass loss in near-zero gravity is that NASA found that treadmill running in space did not abate bone loss. So bone loss in spaceflight was due to the lack of gravity placing a mechanical stress on bones. Extrapolating this to Earth-based models, bed rest, which is often prescribed to patients for treatment of clinical maladies, and particularly in “retirement” living, lack of weight bearing could increase susceptibility to bone fractures.

D. Mortality Occurence Rates From Hip Fractures Predominantly Later in Life

Hip fractures are predicted to reach more than 6 million by 2050, compared with ~1.66 million in 1990 (98), with Asia accounting for 55% of fractures of all types worldwide (147, 233). Mortality progressively increases with postoperative duration after hip fracture. In one study, overall mortality was 10.5% 30 days post-surgery and increasing to 73.6% 7 yr post-surgery (379). Following surgery for femoral neck fractures in 1984–98, other studies found male fatality rates at 30 days increased from 4% at 64–69 yr old to 31% in those aged ≥90 yr (426), and reductions in quality of life due to broken hips are equivalent to reductions for multiple sclerosis or Parkinson’s disease (189). Interestingly, males had greater mortality than females, up to 5–10 yr after hip fracture (5, 82, 377).

E. Mechanisms for Physical Inactivity’s Effect on Bone

While it has been known for over 100 yr that mechanical loading of bone is necessary for optimal bone mass, mechanisms related to bone remodeling are not yet fully elucidated (468). Bone-mass quality and quantity are determined by osteoblasts, osteoclasts, and osteocytes. Osteocytes are the cell type that maintains the balance between bone formation and removal (406), and mice with ablated osteocytes are resistant to unloading-induced bone loss (485). Spatz et al. (468) interpreted the unloading insensitivity of osteocytes as providing evidence that osteocytes play some role in mechanosensation of forces within bone. Importantly, osteocytes transduce the sensed forces from mechanosignals to chemical signals, which, in turn, function to signal modulations of bone mass/strength. Taken together, we suggest that less mechanosensing from physically inactive bones is likely to be transduced to chemical signals that regulate the loss of bone structures. The next paragraph considers molecules that play a role from the mechanosensing process of osteocytes.

It is well established that the maintenance of bone mass over time is a balance between formation of new bone and resorption of old bone. Two important chemical modulators of bone mass in unloading are sclerostin (SOST), produced in osteocytes (96), and receptor activator of nuclear factor kappa-B ligand (RANKL). Sclerostin’s main function is to inhibit bone formation by directly reducing proliferation and differentiation of osteoblasts via its inhibition of the canonical Wnt signaling pathway (216), which enhances bone formation by controlling embryonic cartilage development and postnatal chondrogenesis, osteoblastogenesis, osteoclastogenesis, endochondral bone formation, and bone remodeling (315). Sclerostin mRNA in osteoclasts increases in human serum during bed rest (467), in plasma from patients with chronic spinal cord injury (344), and with hindlimb unloading of the mouse tibia (427). Sclerostin antibodies increased bone mass by increasing bone formation in hindlimb unloaded mice (466). Sclerostin also selectively suppressed Wnt/beta-catenin signaling, osteoblast activity, and viability of osteoblasts and osteocytes (299). Sost-deficient mice have increased bone formation and bone strength (296) and rescue mechanical unloading-induced bone loss (299, 362).

RANKL is a member of the TNF superfamily and is the ligand that binds to RANK, the osteoclast cell-surface receptor. RANKL/RANK signaling controls osteoclast replication, activation, and survival during normal bone modeling and remodeling, and also in conditions of increased bone turnover/remodeling (61). Upregulation of pyruvate dehydrogenase kinase 4 during bone unloading is a molecule that induces Rankl expression in osteoblasts (529). Moriishi and co-workers (529) performed a series of experiments in the femurs of wild-type mice that had undergone tail-suspension and noted that when the tibia bone was unloaded, osteoclastogenesis was enhanced and osteoblast function was inhibited, leading to bone loss in wild-type mice. They further observed that Rankl expression in osteoblasts in the unloaded tibia bone was increased along with increased Sost expression in osteocytes. Intriguingly, Moriishi and co-workers (529) found that overexpression of B cell lymphoma 2 (Bcl2) rescued the unloading phenotype in the wild-type mice described above. Femurs in tail-suspended Bcl2 transgenic mice had wild-type values for osteoblastic and osteoclastogenic functions, Sost expression in osteocytes, Rankl expression in osteoblasts and femur bone mass. Komori concluded: “osteocytes are responsible for bone loss in unloaded condition, and osteocytes augment their functions by further stimulating osteoclastogenesis and further inhibiting osteoblast function, at least partly, through the upregulation of receptor activator of nuclear factor-kappa B ligand (RANKL) in osteoblasts and Sost in osteocytes in unloaded condition” (259).

In addition, several subtypes of bone marrow cells support bone immune cell functions. One subtype, in particular, has a role as a primary lymphoid organ, supporting lymphoid development (334). Lescale et al. (293) published that mechanical unloading causes a decrease in B-cell progenitor populations and the generation of B lymphocytes in the bone marrow, which is concurrent with bone remodeling. The change was cell-type specific as no change occurred in hematopoietic stem cells or multipotent hematopoietic progenitor populations.

A. Physical Inactivity Depresses Some Components of the Immune System

Possibly more than any other body system, the immunity adheres to the U-shaped risk curve as it relates to physical activity. Highest immune suppression (i.e., greatest susceptibility to communicative diseases, such as upper respiratory tract infections) is at the top of both arms of “U” shape. Too little inactivity can elicit immune system suppression, while very high volume physical activity may also have immunosuppressive effects. Human data show that a regular, moderate amount of physical activity has best positive immune response on the bottom of “U”-shaped curve. Inactivity causes an increase in visceral adipose tissue (133, 486), which is associated with atherosclerosis, cancer, dyslipidemia, hypertension, mortality, and T2D, when compared with the healthier peripheral obesity (237, 388, 461). Importantly, some normal weight humans having a high ratio of central-to-peripheral fat have an increased prevalence of being insulin resistant (232). In a cohort study of 334,000 European men and women, the inactive group had 25 and 21% higher hazard ratios than the moderately inactive groups in both the abdominally lean and abdominally obese groups, respectively (133). Pedersen and Febbario (385, 388) proposed “. . . physical inactivity (leads to) accumulation of visceral adipose tissue and consequently to the activation of a network of inflammatory pathways, which promote development of insulin resistance, atherosclerosis, neurodegeneration and tumour growth and, thereby, promote the development of a cluster of chronic diseases.” Overall, physical inactivity leads to visceral adipose tissue accumulation, activating inflammatory pathways. Inflammation initiates the development of systemic insulin resistance, neurodegeneration, atherosclerosis, and tumor growth, thereby facilitating development of clusters of chronic diseases (FIGURE 3).