The goal of this review is to provide an overview of the biological
control of voluntary exercise and spontaneous physical activity
(SPA) in relation to total daily energy expenditure (DEE), food
consumption and obesity in humans and laboratory rodents. In so
doing, we bring together areas of the literature that rarely overlap,
even though are clearly relevant to one another.
By biological control we mean that, in a given environment,
two individuals will exhibit an innately different behavioural or
physiological state, or will respond differently to a change in that
environment. In this context, environment refers to all external
and internal factors that influence (and alter) development and
functioning within an individual from fertilization (formation of
the zygote), or even before. Such an idealized definition of
biological control is essentially impossible to make operational
because we cannot ensure that the environment is 100% identical
for multiple individuals. Nonetheless, careful experimental and/or
statistical control can allow us to probe the extent of biological
control in various behavioural or physiological phenotypes.
Considerable evidence, reviewed below, demonstrates that both
voluntary exercise and SPA are under substantial biological
control in both humans and rodents (Rowland, 1998; Thorburn
and Proietto, 2000; Eisenmann and Wickel, 2009; Swallow et al.,
2009; Feder et al., 2010). However, few studies have examined
the relationship between voluntary exercise and SPA, especially
in rodents, in spite of the fact that compensatory interactions
between voluntary and spontaneous activity could have major
implications for overall energy expenditure. For many phenotypes,
the effects of genetic variation among individuals play a major
role in biological control, so we also briefly review the evidence
The Journal of Experimental Biology 214, 206-229
© 2011. Published by The Company of Biologists Ltd
The biological control of voluntary exercise, spontaneous physical activity and daily
energy expenditure in relation to obesity: human and rodent perspectives
Theodore Garland, Jr1,*, Heidi Schutz1, Mark A. Chappell1, Brooke K. Keeney1, Thomas H. Meek1,
Lynn E. Copes2, Wendy Acosta1, Clemens Drenowatz5, Robert C. Maciel1, Gertjan van Dijk4,
Catherine M. Kotz3and Joey C. Eisenmann5
1Department of Biology, University of California, Riverside, CA 92521, USA, 2School of Human Evolution and Social Change,
Arizona State University, AZ 85287, USA, 3Minneapolis VA Medical Center and University of Minnesota, One Veterans Dr,
Minneapolis, MN 55417, USA, 4Center for Behavior and Neurosciences, Neuroendocrinology Unit, University of Groningen,
Kerklaan 30, 9751 NN Haren, The Netherlands and 5Departments of Kinesiology and Pediatrics & Human Development,
Michigan State University, MI 48824, USA
*Author for correspondence (email@example.com)
Accepted 4 November 2010
Mammals expend energy in many ways, including basic cellular maintenance and repair, digestion, thermoregulation, locomotion,
growth and reproduction. These processes can vary tremendously among species and individuals, potentially leading to large
variation in daily energy expenditure (DEE). Locomotor energy costs can be substantial for large-bodied species and those with
high-activity lifestyles. For humans in industrialized societies, locomotion necessary for daily activities is often relatively low, so
it has been presumed that activity energy expenditure and DEE are lower than in our ancestors. Whether this is true and has
contributed to a rise in obesity is controversial. In humans, much attention has centered on spontaneous physical activity (SPA)
or non-exercise activity thermogenesis (NEAT), the latter sometimes defined so broadly as to include all energy expended due to
activity, exclusive of volitional exercise. Given that most people in Western societies engage in little voluntary exercise,
increasing NEAT may be an effective way to maintain DEE and combat overweight and obesity. One way to promote NEAT is to
decrease the amount of time spent on sedentary behaviours (e.g. watching television). The effects of voluntary exercise on other
components of physical activity are highly variable in humans, partly as a function of age, and have rarely been studied in
rodents. However, most rodent studies indicate that food consumption increases in the presence of wheels; therefore, other
aspects of physical activity are not reduced enough to compensate for the energetic cost of wheel running. Most rodent studies
also show negative effects of wheel access on body fat, especially in males. Sedentary behaviours per se have not been studied
in rodents in relation to obesity. Several lines of evidence demonstrate the important role of dopamine, in addition to other neural
signaling networks (e.g. the endocannabinoid system), in the control of voluntary exercise. A largely separate literature points to
a key role for orexins in SPA and NEAT. Brain reward centers are involved in both types of physical activities and eating
behaviours, likely leading to complex interactions. Moreover, voluntary exercise and, possibly, eating can be addictive. A growing
body of research considers the relationships between personality traits and physical activity, appetite, obesity and other aspects
of physical and mental health. Future studies should explore the neurobiology, endocrinology and genetics of physical activity
and sedentary behaviour by examining key brain areas, neurotransmitters and hormones involved in motivation, reward and/or
the regulation of energy balance.
Key words: dopamine, endocannabinoid system, energy budget, leptin, non-exercise activity thermogenesis, orexin, personality, wheel running.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
207 Exercise, spontaneous activity and obesity
for genetic effects on physical activity. We begin with some
Defining voluntary exercise and SPA
Locomotion is a defining characteristic of animal life, and in most
mammalian species it constitutes a key element of daily life as
individuals search for food, shelter and mates, interact with
competitors and avoid predators. This sort of physical activity can
be termed obligatory (e.g. FAO, 2004). One can also think of
discretionary activity or voluntary exercise, which we define as
locomotor activity that is not directly required for survival or
homeostasis and not directly motivated by any external factor.
Human voluntary exercise occurs in a seemingly endless variety of
ways (e.g. sports) and varies greatly in both intensity and duration,
both of which affect its energetic cost and may modulate its other
In humans, the motivation for voluntary exercise can be
multifactorial, exceedingly complex (Dishman, 2008) and related to
major personality traits (Rhodes and Smith, 2006) (see below). It can
also be rewarding (i.e. psychologically and/or physically) and
apparently even addictive [Aidman and Woollard, and others (Aidman
and Woollard, 2003; Brene et al., 2007; MacLaren and Best, 2010)
and references therein]. In domesticated rodents under laboratory
conditions, one presumes that the motivation for voluntary exercise
– usually measured by wheel running – is simpler (e.g. societal effects
are absent), but it can nonetheless be related to personality traits (Jonas
et al., 2010b) (see below). Wheel running is clearly rewarding for
rodents, and comparative psychologists have considered it to represent
the classic self-motivated behaviour. Sherwin has reviewed evidence
indicating that various species are often highly motivated to run on
wheels, even in the absence of any external reward (Sherwin, 1998).
Importantly, wheel running is not a behaviour exhibited only by
laboratory strains of rodents (e.g. Dewsbury et al., 1980), and some
species of wild rodents run more on wheels than even those that have
been bred specifically for high wheel running (Garland, 2003).
Evidence is accumulating that wheel running can be addictive in
rodents [Werme et al. and others (Werme et al., 2000; Brene et al.,
2007; Kanarek et al., 2009; De Chiara et al., 2010) and references
therein], and it is clear that removing wheel access from animals that
have had it for some period of time can lead to changes in behaviour
(e.g. Malisch et al., 2009) and in the brain [e.g. on neuronal activity
in specific regions (Rhodes et al., 2003a)].
Humans and rodents engage in much physical activity that does
not qualify as voluntary exercise [e.g. fidgeting, non-specific
ambulatory behaviour (pacing)], and this is often termed SPA (e.g.
Ravussin et al., 1986; Kotz et al., 2008). Again, such activity can
vary greatly in both intensity and duration. Recently, Levine et al.
emphasized the importance of NEAT, which they describe as
follows: ‘Physical activity thermogenesis can be subdivided into
volitional exercise (sports and fitness-related activities)
thermogenesis and what we characterize as nonexercise activity
thermogenesis (NEAT).... NEAT is the thermogenesis that
accompanies physical activities other than volitional exercise, such
as the activities of daily living, fidgeting, spontaneous muscle
contraction, and maintaining posture when not recumbent’ [(Levine
et al., 1999) p. 924]. In the present review, we are not generally
concerned with thermogenesis, temperature regulation or heat
balance per se, but the same general distinctions apply when we
consider physical activity (i.e. some aspects are obligatory and others
discretionary or voluntary).
In subsequent papers, the definition of NEAT in humans has been
made even more inclusive, e.g. “NEAT is … akin to the energy
expenditure of spontaneous physical activity. For a human it would
be … performing all of our daily tasks such as walking, talking,
yard work, and fidgeting” [(Levine et al., 2003) p. 169 (see also
Donahoo et al., 2004)]. Under this sort of very broad definition,
lots of NEAT can be voluntary and even encompass what is
commonly viewed as exercise (Kotz et al., 2008) (see also Johanssen
and Ravussin, 2008; Owen et al., 2010). We believe that NEAT
should be more narrowly defined in order to facilitate study of
logically and operationally separable components of energy
With respect to animals, Novak and Levine [(Novak and Levine,
2007) p. 924 (see also Levine et al., 2003)] argued that: “Because
animal [sic] do not have volitional exercise per se, we define animal
NEAT as encompassing the energy expenditure of all activity,
including spontaneous, locomotor, and more stationary or repetitive
activities such as grooming. All animal physical activities require
energy expenditure and are subject to biological regulation and thus
are a part of NEAT and equivalent to human NEAT.” Noting that
wheel running and SPA are not equivalent (Sherwin, 1998), they
express concern that “a running wheel introduces confounding
effects on the amount of activity and energy expenditure that are
independent of energy balance regulation” [(Novak and Levine
2007) p. 924]. Of course, the same may occur in human exercise,
e.g. in exercise addiction or anorexia nervosa, the latter of which
may sometimes be related to dysregulation of physical activity
(activity-based anorexia) (Hillebrand et al., 2008; Kas et al., 2009).
Moreover, following Eikelboom (Eikelboom, 1999), we have argued
elsewhere that voluntary wheel running in rodents may be a
reasonable model of human volitional exercise (Rezende et al., 2009;
Kelly et al., 2010). For example, the day-to-day variability in wheel
running by individual mice from lines selectively bred for high
voluntary wheel running (High Runner lines) and from a non-
selected control line was found to be similar to that observed for
activity levels of free-living human children, adolescents and young
adults, which has been interpreted as evidence that biological
mechanisms influence daily levels of physical activity (Eisenmann
et al., 2009).
Irrespective of how broadly one defines NEAT, it is clear that
we have a large ‘gray area’ between voluntary (or planned) (e.g.
Johanssen and Ravussin, 2008) exercise and SPA, both in humans
and other animals (see also Stubbe and de Geus, 2009; Owen et al.,
2010). For example, in humans, how should we classify mowing
your lawn when you have sufficient financial resources to hire
someone else to do it? What about walking to buy lunch at a
restaurant even though you had brought a sack lunch for the day?
What about play behaviour, which occurs commonly in the young,
and sometimes in the adults, of most mammals (Byers and Walker,
1995), including laboratory house mice (Walker and Byers, 1991);
hyperactivity in the attention-deficit hyperactivity disorder (ADHD)
child; and exercise addiction or activity-based anorexia?
Our conclusion from such considerations is that further scientific
progress will be facilitated by the development of operational
definitions for the components of overall physical activity (see also
Tou and Wade, 2002). In other words, we need definitions that are
tied, more or less directly, to measurement protocols (Table1). We
also need to pay close attention to whether activity is measured as
duration, intensity or a combination, as these can have differential
effects on, for example, energetic costs (e.g. Koteja et al., 1999b;
Rezende et al., 2009), be differentially affected by pharmacological
interventions (e.g. Keeney et al., 2008) and can have different genetic
bases (e.g. Nehrenberg et al., 2009b; Kelly et al., 2010; Leamy et
al., 2010). Moreover, if animal models are to be used to elucidate
THE JOURNAL OF EXPERIMENTAL BIOLOGY
the human condition, then we need to make claims – and eventually
support them with data on underlying mechanisms – regarding how
behavioural phenotypes equate across species (see also Dishman,
2008). Of course, one can also view the situation in reverse, e.g.
by noting that studies of human voluntary exercise may serve as
good models for understanding the basis of wheel running in other
species (see also Eikelboom, 1999).
As noted by Stubbe and de Geus, “Operational definitions of
exercise behaviour have differed strongly across studies” [(Stubbe
and de Geus, 2009) p. 343]. In humans, voluntary exercise is
quantified in various ways, including questionnaires, surveys,
diaries, direct observation, motion sensors (pedometers and
accelerometers), heart-rate monitors and indirect calorimetry
(Table1) (Westerterp, 2009). Each method has advantages and
disadvantages. Issues of reliability, validity and feasibility are
important considerations. In general, there is an inverse relationship
between validity and feasibility. For instance, a feasible method in
large population studies, such as a questionnaire, is probably the
least valid measure (e.g. Shephard, 2003) (see also Ebstein, 2006);
however, the most valid measures of energy expenditure (as opposed
to voluntary exercise per se), such as indirect calorimetry (e.g.
Joosen et al., 2005), are neither practical nor feasible in large studies.
Given the various methodologies and limitations of measuring
physical activity, it is difficult to compare studies. Moreover, studies
that impose relatively greater experimental control on their subjects
with respect to certain factors (e.g. instructing subjects to refrain
from voluntary exercise and/or confining them to metabolic
chambers or hospital wards) may sacrifice face validity with respect
to the phenomenon they wish to study (e.g. behavior and physiology
of free-living subjects).
In captive rodents, voluntary exercise (as we define it) has been
measured almost exclusively using running wheels, which may come
in a variety of sizes, shapes, surface textures and configurations
(Sherwin, 1998; De Bono et al., 2006). Depending on the counting
device employed, and with due methodological cautions
(Eikelboom, 2001; Koteja and Garland, 2001), wheel running over
some period of time (e.g. 24h) can be quantified as total revolutions,
the amount of time activity and/or the average intensity of activity
(e.g. Girard et al., 2007; Dlugosz et al., 2009; Gomes et al., 2009;
Rezende et al., 2009). With video analysis, details of individual
running bouts and the degree of intermittent locomotion can be
quantified (Girard et al., 2001) (see also Waters et al., 2008).
The assessment of SPA in human subjects has also been
accomplished using pedometers, accelerometers and direct
observation/video analysis (Table1). As noted above, much recent
work concerns the variously defined NEAT, which has been
estimated in a variety of ways, corresponding to different definitions
(e.g. Levine et al., 1999; Levine et al., 2003; Levine and Kotz, 2005;
Levine et al., 2005). In most of these studies, accelerometry or multi-
sensor units are used to capture NEAT.
In captive rodents, SPA in cages has been measured using
photobeams in various configurations, force plates (e.g. Malisch et
al., 2009), video analysis and passive infrared detectors (e.g.
Vaanholt et al., 2008; Gebczynski and Konarzewski, 2009a;
Gebczynski and Konarzewski, 2009b). Here, as elsewhere, one needs
to be extremely careful when reading the literature to note what a
particular study actually means by ‘locomotor activity’, as it is often
used to describe either activity in home cages after a period of
acclimation or habituation (of main interest in the present review)
(see also Kotz et al., 2008), locomotion in acute tests in novel arenas
(e.g. open-field tests) (see also Viggiano, 2008; Hesse et al., 2010)
or even wheel running. Variation exists even within studies focusing
T. Garland, Jr and others
specifically on home-cage activity, ranging from variation in
measurement devices and cages or arenas to the software used to
process and analyse the data. These different aspects of locomotor
behaviour generally do not show strong positive relations with
each other (e.g. Sherwin, 1998; Bronikowski et al., 2001)
(http://phenome.jax.org/). Additionally, locomotion most often
refers to ambulation, but SPA refers to all activities, including non-
ambulatory events, such as grooming and rearing behavior.
Some activities of daily living are commonly termed sedentary
behaviours in humans, including reading books, playing cards,
watching television or videos, using a computer (all of which
typically occur while sitting) or sitting in automobiles (e.g.
Speakman and Selman, 2003; Ford et al., 2005; Prentice and Jebb,
2006; Chaput and Tremblay, 2009; Jackson et al., 2009; Owen et
al., 2010). It is apparent that these sorts of behaviours are not
necessarily the opposites of active behaviours, such as jogging, in
either a psychological or a physiological sense (e.g. see Owen et
al., 2010). In other words, voluntary exercise, SPA and sedentary
behaviours do not necessarily lie along a single continuum or axis
of variation, either phenotypically or genetically. Many sedentary
behaviours expressed by humans, especially those involving man-
made objects or devices, cannot have direct counterparts in rodents.
However, rodents will spend time (and energy) interacting with (e.g.
playing with) objects placed in their cages (similar to young
children playing and interacting with objects), and a substantial
research effort in the area of enrichment has relevance in this context
(Young, 2003). Environmental enrichment can have substantial
effects on brain neurochemistry, even blunting the addictive effects
of certain drugs in mice given enrichment beginning at a young age
(Solinas et al., 2009). Some of the molecules affected by
environmental enrichment (e.g. brain-derived neurotrophic factor)
are also affected by exercise and diet (Johnson et al., 2003;
Pietropaolo et al., 2008; van Praag, 2009; Fahnestock et al., 2010),
but interactions among enrichment, diet and propensity for exercise
or SPA have not yet been studied to our knowledge. Nonetheless,
one can imagine that such interactive effects exist. As reviewed by
Chaput and Tremblay, both sleep (“the most sedentary of human
activities”) and cognitive or knowledge-based work have relatively
low rates of energy expenditure, but the former is associated with
a hormonal profile that facilitates appetite control, whereas the latter
enhances food intake (as does television viewing) (Chaput and
Tremblay, 2009). Apparently, no study has yet attempted to
simultaneously examine voluntary exercise, SPA and analogs of
human sedentary behaviours in a laboratory rodent. Nor are we
aware of any studies that have attempted to quantify these
simultaneously in free-living non-human mammals.
DEE and its components
The first law of thermodynamics states that energy can be transformed,
but it can be neither created nor destroyed. Applied to a living animal,
this means that the energy entering via food consumed (Fig.1) will
be balanced by the sum of energy expended (e.g. in physical activity),
stored as fat or glycogen, and used for maintenance, growth and
reproduction. The so-called energy balance equation (e.g. Schoeller,
2009) is simplified for adult mammals that are not reproducing and
not storing energy, in which case energy intake is balanced by energy
expended on basal metabolic rate (BMR), digestion and subsequent
processing of food [thermic effect of food (TEF)], thermoregulation
and activity energy expenditure (AEE). The BMR accounts for a
substantial proportion of DEE, whereas the TEF is thought to
contribute ~5–10% of DEE (FAO, 2004; Schoeller, 2009). The most
malleable component of DEE is AEE, the main focus of this review.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
209 Exercise, spontaneous activity and obesity
As noted above, AEE can be further divided into the energy expended
in voluntary exercise and SPA, also termed habitual physical activities
of daily living, which generate NEAT.
Locomotor costs can be a substantial portion of DEE for
mammals, especially for large-bodied species and those with a highly
active lifestyle (Garland, 1983; Gorman et al., 1998; Girard, 2001;
Carbone et al., 2005). As illustrated in Figs2 and 3, the energy
expended on voluntary exercise can range from negligible to one-
third of DEE in a mouse and two-thirds of DEE in a human in the
Tour de France cycling race (Saris et al., 1989; Swallow et al., 2001;
Vaanholt et al., 2007a; Vaanholt et al., 2007b; Rezende et al., 2009).
In all cases, except perhaps during extreme human endurance
activities, the energy expended in SPA is also appreciable. It is also
worth noting that the amount of energy expended while sitting (a
sedentary behavior) can be substantial. For example, Widdowson
et al. estimated that young male military cadets, with an average
DEE equivalent to men engaged in ‘moderate work’, spent almost
40% of their time and almost one-third of their energy while sitting
(Widdowson et al., 1954).
The techniques for estimating or measuring DEE in free-living
human subjects include doubly labeled water (DLW), heart-rate
monitoring, accelerometry and multi-sensor devices, and
questionnaires that include physical activity and/or food
consumption (Nydon and Thomas, 1989). Questionnaires yield
estimates of the amount of time spent in various activities, and these
can be translated into energy via time–energy budgets, given
estimates of the costs of various activities (Ainsworth et al., 2000).
Although direct and indirect calorimetry are seen as the most reliable
and accurate methods for assessing energy expenditure (e.g.
Ravussin et al., 1986; Joosen et al., 2005; McDonald et al., 2009),
these techniques are not feasible for individuals moving about in
their normal environment. Hence, DLW is considered the gold
standard for capturing DEE in free-living populations (Speakman,
1997; Nagy, 2001; Nagy, 2005; Westerterp and Speakman, 2008).
However, a limitation of DLW for studies of physical activity is
that, although AEE can be calculated, e.g. as the difference between
DEE and resting metabolism, no information on the nature
(spontaneous or voluntary) or intensity (sedentary, low, moderate,
vigorous) can be ascertained without some sort of additional
information, e.g. from accelerometry. In cold environments, there
may also be thermoregulatory expenditures (shivering) that further
confound DLW-derived estimates of activity. The method of heart-
rate monitoring requires measurement of the relationship between
heart rate and oxygen consumption in an individual across a range
of activities (sedentary to vigorous activity); similarly,
accelerometers or multi-sensor devices depend on calibration of
output (counts, etc.) and energy expenditure as determined by
independent methods (e.g. Freedson et al., 1998; Franks et al., 2003;
Welk et al., 2007; Westerterp, 2009). The least-valid manner of
determining DEE is through subjective self-reported surveys and
diaries. Diet records that allow the calculation of total daily energy
intake (kJday–1) can also be used if no weight gain occurs and energy
balance is maintained.
Methods for determining energy use in free-living rodents or other
animals parallel those used for humans (and have similar advantages
and limitations). DLW is the de facto standard for determining
energy use over long periods (e.g. days). This method yields accurate
estimates of average DEE [often called field metabolic rate (FMR)
for free-living animals], but does not provide a detailed breakdown
of the costs of various activities. Some studies have combined DLW
with time-budget analysis and, if sample size is sufficiently large
and there is substantial variation in both FMR and activity, regression
techniques can give insight into energy costs of particular activities
(e.g. Vehrencamp et al., 1989; Chappell et al., 1993; Girard, 2001).
The DLW method has been used to determine FMR in a wide range
of species and its relationship to animal body size, thermal
environment, ecology and phylogeny have been analysed in
considerable detail (e.g. Nagy et al., 1999; Nagy, 2005). In addition
to DLW, investigators have employed heart-rate monitors to estimate
activity energy costs in numerous species (e.g. Bevan et al., 1994;
Bevan et al., 1995), and accelerometry in various forms has been
used for a limited range of subjects (e.g. Shepard et al., 2008).
Finally, time-budget studies combined with laboratory
measurements of different activity costs (and, where appropriate,
Fecal loss (things not absorbed)
Urinary loss (deamination of amino
acids, yielding urea, uric acid and ammonia)
Absorbed food molecules
(e.g. amino acids) available to the
animal for ATP production or as molecules
Thermic effect of food
(gut motility, enzyme secretion, fluid secretion and
absorption, nutrient absorption and processing)
Fig.1. Partitioning of consumed food energy. Note that energy going to the
thermic effect of food is not available for ATP production or biosynthesis,
but it can be used for thermoregulation. In addition, a considerable portion
of the chemical potential energy in absorbed food molecules is lost as heat
during the production of ATP (but again, some of this heat may be used for
Daily energy expenditure (%)
Fig.2. (A,B)Partitioning of daily energy expenditure in a sedentary human
and a sedentary laboratory mouse. The human engages in negligible
voluntary exercise, and the mouse is housed without a wheel. At room
temperature (~21°C), people wear appropriate clothing and so do not have
any extra energy expenditure to maintain body temperature. For mice,
however, 21°C is below their thermoneutral zone, so they have a
substantial cost of thermoregulation (e.g. see Hart, 1971; Hudson and
Scott, 1979; Lacy and Lynch, 1979). The values depicted are
approximations, based on the synthesis of a number of sources (e.g.
http://www.fao.org/docrep/007/y5686e/y5686e04.htm) [Garland and others
(Garland, 1983; Saris et al., 1989; Hammond and Diamond, 1997; Gorman
et al., 1998; Girard, 2001; Swallow et al., 2001; Donahoo et al., 2004;
Westerterp, 2004; Carbone et al., 2005; Vaanholt et al., 2007a; Vaanholt et
al., 2007b; Johanssen and Ravussin, 2008; Rezende et al., 2009; Secor,
2009) and references therein]. BMR, basal metabolic rate; NEAT, non-
exercise activity thermogenesis; SPA, spontaneous physical activity; TEF,
thermic effect of food.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
biophysical modeling of thermoregulatory costs) have also been used
to estimate DEE (e.g. Goldstein, 1988). Comparisons with DLW
suggest that carefully done time-budget studies can be quite accurate
(e.g. Weathers et al., 1984).
In summary, both voluntary exercise and SPA can be important
components of DEE. Apparently, the latter can still be important
even when exercise is high. Over the long term, relatively small
alterations in either component of physical activity can lead to weight
gain (or loss), as can small changes in energy intake (Hill et al.,
2003). However, many human studies have shown that, beyond
approximately 1year, the amount of weight loss predicted to occur
following a sustained increase in energy expenditure or a decrease
in energy intake is often not realized, implying that other changes
must be occurring, e.g. in hunger, satiety or dietary adherence
(Schoeller, 2009). Moreover, alterations in specific dietary
components can have effects that go far beyond their effects on
energy intake [Jebb (Jebb, 2007) and references therein] (Meek et
al., 2010). Hence, prospective use of the energy balance equation
can be a risky proposition with respect to bringing about reductions
in human body fat (Schoeller, 2009).
Voluntary exercise, SPA and energy expenditure in human
In studies of human subjects confined within metabolic chambers
for 24h, Ravussin et al. found that SPA varied widely and was a
rather strong positive predictor of DEE (Ravussin et al., 1986). They
commented (p. 1577) that: “Because the subjects were not allowed
to carry out physical exercise such as isometric exercises or
calisthenics, it is possible that such activity represents an
unconscious need to be active.” The implication is that forced
reduction in voluntary exercise may lead to an increase in other
types of physical activity. Of course, it is also possible that an
increase in voluntary exercise would be accompanied by a decrease
T. Garland, Jr and others
As AEE is a major component of DEE (Figs2 and 3), it is often
assumed that if one initiates (or increases) structured exercise
training or voluntary exercise in free-living humans, then DEE will
necessarily increase. Of course, SPA (or NEAT) may decrease
during the remaining portion of the day (e.g. a compensatory
behaviour or mechanism that homeostatically controls DEE)
following voluntary exercise, thus resulting in no change in overall
AEE or DEE. This has not gone unnoticed in the literature (reviewed
in Westerterp and Plasqui, 2004; Westerterp, 2008; Schoeller, 2009).
Other human studies have addressed the question more from the
perspective of appetite and resting metabolic rate (e.g. Speakman
and Selman, 2003; King et al., 2007a; King et al., 2007b), and related
studies have examined rodents forced to work for food (Vaanholt
et al., 2007b). During free-living daily life, the amount of energy
expended during human locomotion and SPA (i.e. AEE) can also
be greatly impacted by cultural and/or societal factors and
environmental conditions (Dishman, 2008), including the so-called
‘built environment’ (Sallis and Glanz, 2006). Rowland suggested
that habitual physical activity (and DEE) is subject to substantial
biological control (Rowland, 1998). He coined the term ‘activity
stat’ to refer to a hypothetical control center, maintaining energy
expenditure at a particular set point via regulatory changes in SPA,
resting energy expenditure and/or AEE via voluntary exercise.
Others have noted that activity levels may sometimes be a
consequence of, rather than a contributor to, body weight (e.g. Tou
and Wade, 2002). Long-term voluntary exercise does not
consistently affect resting metabolic rate in humans (Speakman and
Studying the relationships between voluntary exercise, SPA and
DEE in humans can be accomplished either by experimental
exercise-training studies or by observational studies of habitual
physical activity. In the former, pre- and post-exercise measures of
DEE (and its components) are assessed, whereas in the latter DEE
and its components can be monitored over a period of time [e.g.
what happens to DEE and NEAT during a particular day when
moderate-to-vigorous physical activity (MVPA) increases?].
Most studies examining this question have measured DEE in free-
living subjects using DLW (Speakman, 1997). If DEE does not
change with the addition of exercise training, then (barring changes
in thermoregulatory costs) that implies that other activities of low-
to-moderate intensities (i.e. SPA) must be reduced during the day,
although there is no specific measure of these activities available
from a DLW study in and of itself. In addition, DLW cannot
document the energy expenditure or MVPA from the exercise-
training portion of the day. This methodological issue can be
resolved by using an accelerometer/multi-sensor device (e.g.
ActiGraph accelerometer, Pensacola, FL, USA).
Summary of experimental studies with humans
In general, the effect of exercise training on non-training physical
activity seems to be age dependent, with no change in DEE in older
adults and an increase in DEE in younger subjects (Westerterp, 2008)
(see also Schoeller, 2009). The matter of the contribution of the
exercise vs non-training activity to the total AEE and DEE was
addressed by Westerterp’s research group using an additional
physical activity measure with DLW (Meijer et al., 1999; Meijer et
al., 1991). They used the Tracmor tri-axial accelerometer, a device
the size of a small pager that is worn on the waist. Their results
provide evidence that older individuals compensate for increased
exercise training by lowering their non-training activity during the
remainder of the day, whereas no such compensation occurs in
younger subjects, who show an increase in both accelerometer output
Daily energy expenditure (%)
Fig.3. (A,B)Partitioning of daily energy expenditure (DEE) when the
amount of voluntary exercise is extraordinarily high. For humans, this
occurs during the Tour de France cycling race (Saris et al., 1989) and for
mice it represents the High Runner lines housed with wheel access
(Swallow et al., 2001; Vaanholt et al., 2007a; Vaanholt et al., 2007b;
Rezende et al., 2009). For the mice, some of the heat produced during
wheel running is used for thermoregulation, thus reducing costs of
thermoregulation per se. SPA is still a substantial part of the energy budget
for mice (Rezende et al., 2009). SPA may also appreciable for these
human ultra-racers, but it has not been directly measured to our
knowledge. In general, larger-bodied mammals are predicted to expend a
larger fraction of their DEE on costs of locomotion, based on previous
allometric analyses (Garland, 1983; Goszczynski, 1986). For additional
literature sources, see Fig.2 legend.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
211 Exercise, spontaneous activity and obesity
and DEE. More specifically, the average accelerometer output of
16 women and 16 men (age 28–41) increased after 8 and 20weeks
(Meijer et al., 1991), and this increase was attributed to the exercise-
training program because non-exercise activity remained fairly
stable. By contrast, total physical activity did not change in 55- to
68-year-old subjects (19 women and 18 men) who decreased their
non-exercise activity (Meijer et al., 1999). However, a recent report
provides conflicting results. Hollowell et al. reported an increase in
total AEE (DEE was not measured) in sedentary, overweight, older
subjects (mean age53years) randomly assigned to groups of
inactive control, low-amount/moderate-intensity, low-amount/
vigorous-intensity or high-amount/vigorous-intensity aerobic
exercise (Hollowell et al., 2009). The intervention lasted 8months,
substantially longer than previous studies (e.g. 7–14weeks).
Studies of free-living humans
The previous section considered the effects of structured exercise
training on DEE and on non-training activity, but of equal interest
is the normal activity of ‘ordinary’ people not engaged in a specified
exercise-training regimen (free-living, unmanipulated humans).
In general, DEE is relatively stable from day to day, despite
considerable variation in the amount of energy expended in MVPA.
In a meta-analysis of 21 DLW studies, the mean within-individual
coefficient of variation for DEE was 12%, ranging from 6.5–22.6%
(Black and Cole, 2000). Hence, on days when MVPA levels are
high, the amount of low-intensity activity (e.g. fidgeting, SPA,
NEAT) is reduced; conversely, on days when MVPA levels are low,
the amount of low-intensity activity is increased.
These results have been replicated in young adults using a 3-day
diary reporting method. Results showed a large coefficient of
variation for energy expended during MVPA (84%), but a small
coefficient of variation for DEE (12%) (Wickel and Eisenmann,
2006). Therefore, when energy expended during MVPA was higher,
the amount of energy expended during inactivity was lower, such
that some degree of compensation occurred for DEE. Fig.4 shows
the negative relationship between the amount of energy expended
during inactivity and during MVPA. Importantly, the slope is
shallower than –1 (dashed line), indicating that compensation for
increased energy expenditure during days with high MVPA is not
Voluntary exercise, SPA and food consumption in laboratory
Access to wheels has been shown by many studies to increase food
consumption in rodents (e.g. Tokuyama et al., 1981; Bell et al., 1997),
including in the High Runner (HR) lines of mice that have been
selectively bred for high daily wheel running, as well as their non-
selected control lines (Koteja et al., 1999b; Swallow et al., 2001).
However, in female DBA/2J mice, which ran only 1–2kmday–1, food
consumption was not significantly increased. This anomalous finding
may be attributable to the level of activity being too low to stimulate
food intake (Jung et al., 2010) (see also Jung and Luthin, 2010) (on
C57Bl/6 mice). Even with the concomitant increase in food
consumption, access to a running wheel, particularly in males and
especially if access begins at a young age, tends to reduce growth,
body mass and body fat in both the HR and control lines of mice
[Tokuyama et al. and others (Tokuyama et al., 1981; Bell et al., 1997;
Koteja et al., 1999b; Swallow et al., 1999; Swallow et al., 2001; Kelly
et al., 2006) and references therein]. However, this effect is not always
observed in rodents. In a recent report, wheel access had no significant
effect on % body fat in young male C57Bl/6 mice [Jung and Luthin
(Jung and Luthin, 2010) and references therein]. The cause of such
discrepant results deserves systematic investigation. As previously
noted for HR and control mice measured for wheel running, body
mass and apparent food consumption weekly from ages 4 to 84weeks,
the mean ontogenetic trajectories for food consumption are more
complex than those for either wheel running or body mass (Morgan
et al., 2003). In any case, if wheel access causes a significant increase
in food consumption, then we take that as prima facie evidence that
the energy cost of wheel running is not fully compensated by reduced
cage activity (see also Kumar et al., 2010). In this context it is
important to note that long-term voluntary exercise does not
consistently affect resting metabolic rate in rodents (Speakman and
Selman, 2003; Kane et al., 2008).
Forced treadmill exercise can affect SPA in rats [Kotz (Kotz, 2008)
and references therein], but because very few studies report
simultaneous measurements of SPA and wheel running, the extent
to which reductions in SPA may partially counteract various effects
of voluntary exercise on wheels is currently unclear. Lachmansingh
and Rollo found a negative, non-significant correlation between time
spent in wheel running and in non-wheel running locomotion in
transgenic mice with high growth rates (r–0.01) and in their normal
siblings (r–0.15), but when transgenic and normal mice were
pooled, the correlation was significantly positive (r0.45)
(Lachmansingh and Rollo, 1994). Koteja et al. found that, after 6weeks
of acclimation, access to a free vs locked wheel reduced cage
locomotion in both HR and control lines of mice, especially in males,
based on focal-animal scan observations (Koteja et al., 1999a). Harri
et al. reported that adult male C57BL/6J mice ran for an average of
114min in a 24-h period, while spending an additional 22min feeding
on the cage floor, compared with mice without wheel access (Harri
et al., 1999). The extra time spent running and feeding was deducted
from time spent resting, climbing/feeding on the cage lid, locomoting
on the cage floor and grooming. Weight gain and food consumption
Energy expended in inactivity (kcal kg–1 min–1)
Energy expended in MVPA (kcal kg–1 min–1)
05 1015 20253035 4045
Fig.4. Relationship between estimated energy expenditure during inactivity
and during moderate-to-vigorous physical activity (MVPA) in 125 human
males and 152 females aged 18–24years [data from Wickel and
Eisenmann (Wickel and Eisenmann, 2006)]. On days when MVPA was
relatively high, DEE was also relatively high [see fig.3 in Wickel and
Eisenmann (Wickel and Eisenmann, 2006)], but, as shown here, some
compensation occurred in that the energy expended during inactive
behaviours was reduced. However, the slope (–0.25) is much shallower
than –1 (dashed line), indicating that compensation was incomplete.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
were similar for wheel-access and control groups, indicating that wheel
running can substitute for other forms of energy-consuming
behaviours and vice versa. However, these mice were running only
2kmday–1, and higher amounts of wheel running, typical of some
other inbred strains, may lead to different results (see also Festing,
1977; Lightfoot et al., 2004; Jung et al., 2010; Jung and Luthin, 2010).
de Visser et al. found that, during a 6-day observation period, female
C57BL/6J mice decreased home-cage activity when allowed
simultaneous access to a wheel, but increased the total amount of
time in activity (cage plus wheel) (de Visser et al., 2005).
We emphasize that laboratory strains of house mice have been
evolving for many generations in relatively small cages without a
wheel. Such conditions have been in force since their ancestors were
first brought into laboratory culture in the early 1900s, or even
earlier. Thus, laboratory mice have been evolving in the absence of
any opportunity for voluntary exercise. We estimate the number of
generations in captivity as approximately 400 (100years ?
4generations per year).
Many aspects of the phenotype are known to evolve during
domestication and/or adaptation to laboratory culture [Garland et
al. (Garland et al., 1995) and references therein]. Given how rapidly
this evolutionary change can occur (Hare et al., 2005; Albert et al.,
2009; Simoes et al., 2009), laboratory strains of mice may now be
rather well adapted to this low-activity environment, in which they
do not need to move long distances to secure food, other resources
or mates, and they do not have to avoid or escape from predators.
Many (perhaps most) populations of modern Homo sapiens are
living in similar conditions. We estimate the number of reduced-
activity generations as perhaps 10–25 (200–500years divided by
20years per generation) (see also Booth et al., 2002b), far fewer
than for laboratory mice. It is possible that some evolutionary
adaptations may have occurred in response to the altered selective
regime even over this relatively small number of generations (Byars
et al., 2010). Nevertheless, it is hard to imagine that the modern
human genome is optimally adapted to a low-activity lifestyle, and
it is thus not surprising that low levels of physical activity are
associated with a broad range of human diseases and disorders
(Booth and Lees, 2007). As Booth and colleagues have argued,
physical activity is probably a prerequisite for normal physiological
gene expression (Booth et al., 2002a; Booth et al., 2002b). The
foregoing leads to the disturbing perspective that the normal state
for laboratory house mice is the absence of voluntary exercise,
whereas that for humans is the presence of exercise. If valid, this
perspective would call into question numerous studies of mice that
have tried to draw inferences about human physiology and
behaviour, and vice versa.
Energy intake, physical activity and obesity
The current human obesity epidemic in industrialized countries is
stunning in its magnitude and probable effects. For example, healthy-
weight adults are now in the minority in the US (Schoeller, 2009).
The cost of this increase in obesity and related conditions and diseases
has been estimated to approach 100 billion dollars in the US alone
(Finkelstein et al., 2003). Moreover, it has been speculated that the
current generation is the first that will, on average, have a lifespan
shorter than that of their parents (Olshansky et al., 2005).
Given the energy balance equation (see DEE and its components),
it seems intuitive that obesity is caused by an increased energy intake
and/or reduced DEE and, more specifically, a reduced level of AEE.
Indeed, cross-sectional, longitudinal and experimental studies have
all demonstrated the role of energy intake and energy expenditure
in human weight gain and/or loss (Zurlo et al., 1992; Miller et al.,
T. Garland, Jr and others
1997; Prentice and Jebb, 2004; Tremblay et al., 2004; Jebb, 2007;
Bray, 2008). An additional factor that contributes to increased energy
intake in many modern human populations is the availability of
highly palatable, energy-dense foods, and Prentice and Jebb argue
for the importance of “interactions between energy-dense diets and
low-levels of physical activity... due to an asymmetry between the
hunger and satiety arms of human appetite control”, which will result
in a disruption of an individual’s energy balance [(Prentice and Jebb,
2004) p. S98]. Needless to say, eating can also be strongly influenced
by hedonic responses (Blundell, 2006) and social facilitation
(Drewett, 2007), and obesity can spread through large social
networks (Christakis and Fowler, 2007). More recently, the possible
stimulatory effect of knowledge-based work on appetite has been
emphasized (Chaput and Tremblay, 2009).
In spite of the received wisdom that industrialized humans have
reduced physical activity levels [e.g. see references in Booth et al.
(Booth et al., 2002a; Booth et al., 2002b)], Westerterp and Speakman
provide evidence that physical AEE has not declined over the same
period that obesity rates have increased, and argue that it is unlikely
that decreased DEE has been a major contributor to the human obesity
epidemic (Westerterp and Speakman, 2008). Furthermore, recent
reviews of the temporal trend in human obesity and the ‘big two’ (i.e.
physical activity and diet) are challenging this traditional view and
offering alternative (or additional) putative contributors, including
microorganisms, epigenetics, increasing maternal age, greater
fecundity among people with higher adiposity, assortative mating,
sleep debt, endocrine disruptors, pharmaceutical iatrogenesis,
reduction in variability of ambient temperatures, and intrauterine and
intergenerational effects (Bray and Champagne, 2005; Eisenmann,
2006; Pomp and Mohlke, 2008; McAllister et al., 2009). Previously,
Sclafani classified nine different origins of obesity in animals: neural,
endocrine, pharmacological, nutritional, environmental, seasonal,
genetic, idiopathic and viral (Sclafani, 1984). Just as there are several
etiological factors acting independently or together in the development
of obesity at any point in time, it is possible that the increase in obesity
was due to the temporal change in a single or combination of
etiological factors in each individual case. One may argue that the
trend can be attributed to changes in several environmental factors
acting and interacting with each other during prenatal and postnatal
growth, puberty and into adulthood. Another factor adding complexity
is that genotype–environment interactions are likely to be common
(see also Dishman, 2008). For example, the relationships between
physical activity, energy intake and body composition can differ
dramatically between the sexes, among individual humans and among
strains of mice, and the effects of exercise training are also known
to vary among individual humans in relation to genotype (Swallow
et al., 2001; Melzer et al., 2005; Girard et al., 2007; Vaanholt et al.,
2008; Nehrenberg et al., 2009a; Jung and Luthin, 2010; Jung et al.,
2010; Meek et al., 2010; Timmons et al., 2010). As noted in the
previous section, wheel access usually, but not always, reduces body
fat in rodents, especially in males.
This leads us to the concept of the obesogenic environment, which
can be defined as “a set of circumstances that encourages people
to eat and drink more calories than they expend and to become
obese” (NHS, 2008). For humans, components of an obesogenic
environment may include ready availability of highly palatable and
energy-dense foods, lack of easy access to the opportunity for
exercise (e.g. no safe parks nearby), use of motorized transportation
for essential activities, social facilitation of eating, social facilitation
of sedentary activities and a lack of education for making good
decisions with respect to diet and exercise. For laboratory rodents,
an obesogenic environment may be considered as one with ad
THE JOURNAL OF EXPERIMENTAL BIOLOGY
213 Exercise, spontaneous activity and obesity
libitumavailability of palatable foods (including standard chow and,
even more so, ‘Western’ diets) (e.g. Meek et al., 2010), housing in
relatively small cages with no need to move to find food or other
resources and the absence of a wheel. Lab rodents are routinely
housed in same-sex groups of a few individuals, and it is possible
that some social facilitation of eating or of sedentary behaviours
occurs, although we are not aware of any studies that specifically
address this issue. As discussed above, it is reasonable to view both
modern humans and laboratory rodents as living in ‘unnatural’
environments relative to their not-so-distant ancestors (see also
Booth et al., 2002a; Booth et al., 2002b; Booth and Lees, 2007).
Instead, an environment that requires, promotes or at least allows
higher levels of physical activity is more attuned to their genetic
complements, perhaps especially for humans. In addition, the
process of domestication and selection of rodents for good breeding
success, large litters, successful parental care, etc., has led to genetic
changes that probably interact with the laboratory environment. In
any case, as noted by de Krom et al., when using animal models to
elucidate aspects of human obesity, it is important to consider
whether and how to model components of obesogenic environments
(de Krom et al., 2009).
In rodents, diets high in fat are routinely used to induce obesity,
although effects can vary substantially among strains (e.g. Brownlow
et al., 1996; Funkat et al., 2004; Svensson et al., 2007; Bjursell et
al., 2008; Novak et al., 2010) (http://jaxmice.jax.org/diomice/
diets.html). The obesogenic effect of high-fat diets is related to their
higher caloric density; although rodents typically decrease the total
amount (mass) of food consumed when given a high-fat diet, they
do not decrease food intake enough to avoid gaining body fat.
Recently, Meek et al. showed that a Western diet (42% of kJ from
fat, plus added sucrose) was obesogenic in males of both HR and
control lines of mice, beyond its effects on caloric intake (Meek et
al., 2010). They converted food consumption in grams to an
equivalent caloric intake, and found that even with caloric intake
and amount of daily wheel running as covariates, statistical analysis
indicated that Western diet caused increased body mass and
retroperitoneal fat pad mass in both HR and control mice. This result
emphasizes the importance of dietary components for weight gain
In human studies, both under- and overeating have been shown
to influence DEE and its components. It is fairly clear that both
DEE and AEE will decrease during energy-restricted diets
(Westerterp, 2008). This reduction in energy expenditure is partly
due to a reduction in body weight with a negative energy balance
(~40%), but the majority (~60%) is a consequence of lower activity
levels. This was perhaps best demonstrated in the semi-starvation
studies at the University of Minnesota following World War II (Keys
et al., 1950).
Although it has long been known that humans differ in the
susceptibility to weight gain in response to overfeeding, few studies
have quantified the changes in AEE in response to overfeeding. As
one example, Leibel et al. chronically over- or underfed human
subjects to produce 10% changes from initial body mass, and did
not restrict exercise (Leibel et al., 1995). Individuals compensated
for this gain or loss both through resting metabolic rate and AEE.
No differences were observed in compensation between non-obese
and obese subjects, and the extent of compensation was greater than
the predicted change in energy expenditure due to adjustments in
body mass. In another experiment, Levine and colleagues overfed
16 sedentary, lean adults (both sexes) an extra 1000kcalday–1for
8weeks, while restricting voluntary exercise. NEAT was estimated
by subtracting of basal and postprandial energy expenditure from
DEE, which was measured by DLW. Subjects displayed changes
in NEAT that ranged from –98 to +692kcalday–1, and 59% of the
variation in fat gain could be statistically explained by the variation
in NEAT (Levine et al., 1999).
With such a wide array of causes to weight gain, future studies
of human susceptibility/resistance to obesity need to be more all
encompassing. For example, they need to determine simultaneously
the effects of voluntary exercise, NEAT/SPA and sedentary
behaviours (including breaks in sedentary behaviour) (e.g. Healy et
al., 2008) on weight gain/loss and body composition, while
simultaneously measuring DEE, food consumption and specific
dietary components (at least macronutrients) with robust methods
(e.g. not based solely on self-reporting). In addition, biomarkers of
appetite and energy regulation (see below) should also be considered.
Effects of age and sex need to be considered with sample sizes
adequate to detect interactive effects with reasonable statistical
power. And, eventually, comparisons among socioeconomic,
cultural and racial groupings need to be performed (e.g. Simonen
et al., 2003a; Stubbe and de Geus, 2009). We recognize that such
studies will be neither easy nor inexpensive, but they are warranted
to better understand the complex nature of energy homeostasis and
body-weight regulation in human populations. Similar studies need
to be performed with rodents as putative models of the human
condition, and similarities/differences elucidated.
Neurobiology of voluntary exercise and SPA
Relative to other areas of exercise physiology, very little is known
about the neurobiology of voluntary exercise, SPA or sedentary
behaviours in healthy animals. Similarly, we are far from
understanding the neurobiology of human ADHD (van der Kooij
and Glennon, 2007). At present, it is widely accepted that the
mammalian brain directs complex behaviours, and that there can
be vast individual variation in these behaviours. However, we lack
a comprehensive understanding of the neurobiological regulation
of motivated behaviours, including voluntary exercise (and eating).
Various neural correlates of generalized locomotor activity have long
been the subject of considerable research, but central themes are
difficult to extract, often because operational definitions of
locomotor activity vary widely in the literature, as has been noted
elsewhere in this paper. For example, studies that purport to address
SPA often do so from the construct of activity in an open-field and/or
in a novel environment, rather than in a standard housing cage with
habituated animals. Although all behaviours within the spectrum of
locomotor activity do involve locomotor activation, it is abundantly
clear that not all are created equal (e.g. Dishman, 1997; Dishman,
2008; Sherwin, 1998; Bronikowski et al., 2001; Ang and Gomez-
Pinilla, 2007; Kotz et al., 2008; Leasure and Jones, 2008; Pietropaolo
et al., 2008). The following sections will discuss neurobiological
underpinnings of both voluntary exercise and SPA, but we include
some studies that involve other measures of locomotion. We also
include a consideration of how aspects of personality may be related
to locomotor behaviour of various types. The endocrine bases of
variation in voluntary exercise and SPA are also of great importance,
but that literature is beyond the scope of the present review (e.g.
see Girard et al., 2007; Levine et al., 2003; Castaneda et al., 2005;
Malisch et al., 2007; Vaanholt et al., 2007c; Vaanholt et al., 2008;
Neurobiology of voluntary exercise
In general, locomotor activity in both humans and rodents is
controlled by a complex cascade of neurochemical interactions that
coordinate central neural impulses with motor outputs. At this point,
THE JOURNAL OF EXPERIMENTAL BIOLOGY
it not known to what extent voluntary behaviour, like eating and
sex, can be considered a classical motivated behaviour. However,
because it can be agreeable to be physically active, a litany of other
neural systems beyond those required for basic motor control
become relevant, including those involved in aversion, conditioning,
learning and the desire for and perception of both exogenous and
During earlier stages of human evolution, locomotion, including
endurance running, was closely linked to caloric acquisition (Dudley,
2001; Bramble and Lieberman, 2004). The reward derived from
exercise could be at least part of the proverbial ‘bringing home the
bacon’. For contemporary humans in Western societies, locomotion
is rarely required for food acquisition, but exercise activities,
including endurance running, are often described as both rewarding
and pleasurable. Moreover, endurance training has been reported
to induce an array of pleasant psychophysical effects, including stress
reduction (Long, 1983), increased anxiolysis and mood improvement
(Ledwidge, 1980). Many athletes claim a pleasurable state of
euphoria following or during sustained endurance-type exercise,
commonly referred to as a ‘runner’s high’ (Morgan, 1985; Dietrich
and McDaniel, 2004). Thus, it has been suggested that evolution
can ‘use’ neurobiological rewards (e.g. euphoria, anxiety reduction,
reduced pain sensation or exercise-induced analgesia) (e.g. Li et al.,
2004) to motivate animals to engage in activities, such as endurance
running, that otherwise may be painful, stressful, energetically
costly, time-consuming or risky (Ekkekakis et al., 2005). This
hypothesis can be partially tested using selective breeding
experiments (Keeney et al., 2008) (for reviews, see Rhodes and
Kawecki, 2009; Swallow et al., 2009; Feder et al., 2010) and through
comparisons of species that differ in their propensity for exercise
under natural conditions (Raichlen et al., 2010).
As previously stated, voluntary wheel running can be a classic
self-rewarding behaviour in laboratory rats and mice (Premack,
1964; Timberlake and Wozny, 1979; Belke and Heyman, 1994;
Belke, 1996; Sherwin and Nicol, 1996; Sherwin, 1998; Ekkekakis
et al., 2005; Brené et al., 2007). Many studies have shown that both
wild rodents and laboratory strains are highly motivated to run on
wheels and will voluntarily run long distances (e.g. Dewsbury et
al., 1980; Rodnick et al., 1989; Lambert et al., 1996; Sherwin, 1998;
Allen et al., 2001; Burghardt et al., 2004; Naylor et al., 2005; Leasure
and Jones, 2008; Rhodes et al., 2005). Similarly, rats show
conditioned place-preference for environments associated with
bouts of wheel running (Lett et al., 2000), as well as the arm of a
T-maze allowing access to a running wheel (Hill, 1961). In addition,
rats and mice can be entrained to perform a variety of tasks to receive
a reward of access to wheel running, including crossing an aversive
water barrier (Sherwin and Nicol, 1996) or lever-pressing (Kagan
and Berkum, 1954; Collier and Hirsch, 1971; Iversen, 1993; Belke
and Garland, 2007).
In extreme cases, exercise can be enjoyable to the point where
it becomes habit-forming, sometimes so much so that it justifies
comparison with traditional addictive behaviours [Werme et al. and
others (Werme et al., 2000; Aidman and Woollard, 2003; Brené et
al., 2007; Kanarek et al., 2009; De Chiara et al., 2010; MacLaren
and Best, 2010) and references therein]. As synthesized by Scheurink
et al., some regular distance runners have reported a progression of
feelings towards exercise similar to those experienced during the
course of addiction to a drug of abuse (Scheurink et al., 2010). The
runners reported sensations of euphoria after strenuous bouts of
exercise, often coincident with a perceived need to increase the
duration or intensity of exercise to achieve similar feelings of well-
being (i.e. they appeared to develop tolerance), followed by
T. Garland, Jr and others
increasing encroachment of the addiction into everyday life (e.g.
difficulties in job performance and social interactions) and, lastly,
symptoms of withdrawal, including depression, irritability and
anxiety, when prohibited from running (Aidman and Woollard,
2003; Allegre et al., 2006). It is worth noting that, like exercise,
some types of overeating may represent a form of addictive
behaviour (Barry et al., 2009; DiLeone, 2009; Johnson and Kenny,
2010; Liu et al., 2010).
It has traditionally been hypothesized that dopamine is the crucial
transmitter for the mediation of reinforcement phenomena
(Salamone and Correa, 2002). Although the literature has yet to
reach a consensus on the behavioural significance of dopamine
(Salamone and Correa, 2002), several lines of evidence highlight
its role in reward (Berridge and Robinson, 1998; Schultz, 2001;
Schultz, 2002), learning (Wise, 2004; Owesson-White et al., 2008),
motivation (Salamone and Correa, 2002; Wise, 2004), emotion
(Sevy et al., 2006), addiction (Hyman and Malenka, 2001; Volknow
et al., 2004) and the control of complex motor movement (Beninger,
1983; Salamone, 1992).
At present, a relationship between dopaminergic signaling and
the performance of voluntary exercise seems fairly well established.
Disruption of dopaminergic transmission in certain parts of the brain
can strongly affect a wide range of locomotor behaviours. For
example, administration of dopamine antagonists systemically or
into the nucleus accumbens, as well as depletions of dopamine within
the nucleus accumbens, reduces exploratory (Ahlenius et al., 1987),
open-field (Correa et al., 2002; Correa et al., 2004), spontaneous,
conditioned and drug-induced locomotor activity in rats (Jones and
Robbins, 1992). When considering voluntary exercise, however,
there remains significant debate about whether an individual’s
dopaminergic profile is a result of or an effecter of activity levels
(Knab and Lightfoot, 2010). For instance, it has been suggested that
differences in activity between inbred strains of mice result from
expression differences of dopamine 1 (D1)-like receptors and
tyrosine hydroxylase (Knab et al., 2009). In a related fashion,
obesity-prone rats have decreased receptor expression, accompanied
by decreased extracellular dopamine levels in the nucleus accumbens
(Geiger et al., 2008; Geiger et al., 2009; Rada et al., 2010). Obese
humans have also been shown to have decreased D2 receptor
expression, based on a series of six single nucleotide polymorphisms
related to D2 receptor expression (Davis et al., 2008; Davis et al.,
2009). However, studies in rats have shown that acute bouts of
exercise increase central dopamine concentrations (Meeusen et al.,
1997), and that these effects are mediated by locomotor speed,
direction and posture (Freed and Yamamoto, 1985).
Work from the Garland laboratory suggests that the HR lines of
mice have an altered reward threshold for wheel running compared
with their non-selected control lines (Belke and Garland, 2007), and
that this difference is related to overall dopaminergic tone (Mathes
et al., 2010). This ‘dysregulation’ could affect one or many of the
factors that influence the sensitivity or reactivity of the neural
network that regulates the anticipation of rewards. These factors
can include the amount of dopamine present, the density and
localization of dopamine receptors, and the rapidity of its transport
back into the cell (Davis et al., 2008). Several studies confirm that
HR mice have diverged with respect to control mice in dopamine
receptor expression, as well as response to drugs that affect
dopaminergic signaling. For example, HR mice had a 20% increase
in mRNA for D2 and D4 receptors in the hippocampus compared
with control mice (Bronikowski et al. 2004). Pharmacological
studies with dopamine transporter blockers found differential effects
on wheel running in HR and control mice, attributed to altered
THE JOURNAL OF EXPERIMENTAL BIOLOGY
215 Exercise, spontaneous activity and obesity
functionality of the D1 receptor system (but not the D2 receptor,
serotonergic or opioidergic systems) (Rhodes et al., 2001; Rhodes
and Garland, 2003; Rhodes et al., 2005; Li et al., 2004). In addition,
HR and control mice also have different wheel-running responses
to D1-like antagonists (Rhodes and Garland, 2003).
The behavioural significance of these observed differences in
dopamine functioning between HR and control mice may be
tantamount to an addiction to wheel running in HR mice (Rhodes et
al., 2005). Consistent with this idea, Fos immunohistochemistry
studies show that, when wheel access is blocked, HR mice have a
greater proportional increase in activity in several brain regions
implicated in reward and motivation, suggestive of a classic state of
withdrawal (Rhodes et al., 2003a). If HR mice are addicted to wheel
running, then it is possible that they run at higher speeds (and thus
achieve greater distances) to achieve the same neurobiological reward
from wheel running that control mice receive during or after lower-
speed running. By this logic, one could hypothesize that HR mice
have decreased dopaminergic functioning, and thus differentially seek
rewards to increase dopamine tone. This is consistent with studies of
humans, in which it has been suggested that individuals with relatively
low dopaminergic function may seek rewarding substances (e.g. such
natural rewards as food or sex, or drugs of abuse) to increase
endogenous dopamine levels and thus ameliorate mood (Blum et al.,
2000; Davis et al., 2008). Of course, it is also possible that HR mice
have increased dopamine functioning relative to controls. As suggested
by Mathes et al., HR mice could run more because they are
hypersensitive to rewards, perhaps as a result of increased
dopaminergic functioning (Mathes et al., 2010). The authors suggest
that this combination may enhance the reinforcement value of a given
reward and thus motivate individuals to further pursue its acquisition.
Consistent with the idea that HR mice may have acquired reward-
related neurobiological alterations over the course of selective
breeding, we have also observed sex-specific differences in the
wheel-running response of HR mice to pharmacological
manipulation of the endocannabinoid system (ECS) (Keeney et al.,
2008). The ECS is a complex endogenous signaling system made
up of transmembrane cannabinoid receptors (CB receptors), their
ligands (endocannabinoids) and proteins involved in synthesis and
modification of endocannabinoids (De Petrocellis et al., 2004; Cota
and Woods, 2005; Demuth and Molleman, 2006). There are two
primary cannabinoid receptors, CB1 and CB2. The CB1 receptor
is the most abundant G protein-coupled receptor expressed in the
brain (Pagotto et al., 2006), with particularly dense expression in
the hypothalamus, pituitary, cerebellum and mesolimbic
dopaminergic reward pathways (Herkenham et al., 1990; Matsuda
et al., 1990; Demuth and Molleman, 2006). Endocannabinoid
signaling via the CB1 receptor is thought to play an important role
in incentive stimuli, in part because of the close involvement of
endocannabinoid signaling with the dopaminergic system (Lupica
and Riegel, 2005; Laviolette and Grace, 2006; Maldonado et al.,
2006; Pillolla et al., 2007). Studies have shown that, in some cases,
both the ECS and the dopaminergic system mutually influence the
performance of locomotor behaviours (Giuffrida et al., 1999;
Beltramo et al., 2000; Gorriti et al., 2005).
Ample evidence supports a relationship between the ECS and
the regulation of physical activity (for review, see Fuss and Gass,
2010). In particular, it is thought that the CB1 receptor may control
the expression of wheel running in rodents (Dubreucq et al., 2010),
as well as exercise in humans (Dietrich and McDaniel, 2004). Hill
et al. have recently shown that voluntary exercise (wheel running)
increases CB1 receptor binding and the tissue content of anandamide
in the hippocampus of rats (Hill et al., 2010). Sparling et al. showed
that a single bout of exercise in trained humans increases blood
levels of anandamide (Sparling et al., 2003). Although these and
other studies demonstrate a clear link between ECS signaling and
exercise behavior, much still remains to be known, particularly in
reference to the directionality of the behaviour–neuropeptide
relationship, as well as the extent of any sex differences.
Neurobiology of SPA and NEAT
Understanding of the neurobiological underpinnings of SPA and
NEAT is in its infancy, but it is clear that the brain plays the critical
regulatory role in determining their levels. Brain lesion and
stimulation studies demonstrate changes in SPA, which translate
into differences in NEAT. Although many brain areas contribute to
SPA, none can be considered in isolation as the brain operates in a
network, such that firing activity in one area – which is positively
or negatively affected by environmental cues or physiology –
ultimately influences firing patterns in other brain areas. Fig. 5
incorporates some of these ideas by showing some of the central
and peripheral mediators that interact to produce SPA and the
resultant NEAT. Behavioural studies can determine the output of
specific brain activity, which will aid in understanding the brain
areas and neurotransmitter systems that play dominant roles. For
instance, in rats, direct electrical stimulation of the ventral tegmental
area and substantia nigra, which have dopaminergic projections to
the nucleus accumbens and striatum, respectively, elevates SPA and
probably NEAT (Kotz et al., 2008). Many neurotransmitters have
been shown to influence SPA, although most studies that report
locomotor activity do not study SPA as a primary endpoint. Rather,
locomotor activity, measured in a beam-break chamber, is often used
as an assessment of non-specific drug effects, e.g. in studies of drugs
of abuse. Similarly, low locomotor activity is used as a diagnostic
criterion for depression or illness in rats and mice (e.g. Malisch et
al., 2009). Thus, the data are not always easy to find, and there is
probably much missing. Further, the energetic consequence of
locomotor activity is often not considered, and thus the relative
impact on body weight cannot be assessed.
Like the complex regulatory network for feeding behaviour, a
network of mediators regulates SPA. Very recent reviews (Kotz et
al., 2008; Teske et al., 2008) have covered the specific data linking
the action of several neuropeptides to SPA and NEAT, and hence
that information will not be repeated here; rather, we will put some
of the information into the context of physiological mechanisms
contributing to obesity and obesity resistance. The studies discussed
in those papers included only literature in which the endpoint was
obtained from animals that had been habituated (usually for >24h)
to a measurement chamber large enough to encourage SPA (i.e.
unlike most home-cage environments). The habituation period to
these chambers reduces the potential novelty-induced anxiety
behavior that is often observed in classic open-field testing
paradigms (Castaneda et al., 2005).
Although not comprehensive, the major neuropeptide systems
(peptide and receptor) that have been studied relative to SPA include
cholecystokinin, corticotropin-releasing hormone, neuromedin U,
neuropeptide Y, leptin, agouti-related protein, orexins and ghrelin.
As mentioned above, dopamine may be the final common signaling
mechanism for the action of all of these neuropeptides. Importantly,
all have established roles in feeding behaviour, and thus the reported
effects on SPA must be considered in the context of concurrent
changes in other behaviours. Relevant to obesity is the proportional
effect of stimulation and/or inhibition of relevant brain pathways
on energy balance. If the energetic consequences of one behaviour
(e.g. SPA) cancel out those of another behaviour (e.g. feeding), then
THE JOURNAL OF EXPERIMENTAL BIOLOGY
the sum effect on energy balance will be nil. More likely, there is
a disproportionate effect, with one elicited behaviour impacting
energy balance more so than the other. Thus, in considering any of
these neuropeptides as obesity targets, one must consider the full
effect of the neuropeptide action.
Orexin A (hypocretin) is one such peptide that has been studied
in detail. Orexin is synthesized in the lateral hypothalamic area,
with projections throughout the hypothalamus and the rest of the
brain (de Lecea et al., 1998; Sakurai et al., 1998). There are two
orexin receptors, also distributed throughout the brain (Sakurai et
al., 1998; Trivedi et al., 1998). Orexin B is derived from the same
preprohormone as orexin A, but its effects are less studied, in part
because its effects are less robust, probably because of its reduced
binding affinity to both orexin receptors relative to orexin A
(Sakurai et al., 1998; Trivedi et al., 1998). The large orexin network
is suggestive of an integrative function for these neurons and the
neuroanatomical origin of a particular orexin neuron population
may define its functions. With respect to feeding behaviour and
SPA, orexin neurons projecting from more caudal medial lateral
hypothalamus regions may be more important to SPA as a result
of projections to the midbrain ventral tegmental area, which
contain dopamine-producing neurons important to locomotor
Orexin A injected into specific brain areas markedly stimulates
SPA (Kotz et al., 2006; Kotz et al., 2008) and NEAT in rats. This
effect is much larger than that for orexin B. Mice lacking orexin
have less SPA and weigh significantly more than wild-type
littermates, despite reduced feeding behaviour (Hara et al., 2001),
suggesting that changes in SPA may have a more profound influence
on body weight than energy intake in these orexin-depleted animals,
and implicating orexin in the regulation of energy balance. SPA
and NEAT induced by orexin A are blocked by pre-administration
of the orexin 1 receptor antagonist SB334867 (Kiwaki et al., 2004;
Kotz et al., 2002; Kotz et al., 2006; Novak et al., 2006; Teske et
al., 2006). Although this suggests that the orexin 1 receptor governs
orexin A-induced effects on SPA and NEAT, the role of orexin 2
receptors cannot be discounted as orexin A binds to both receptors,
and other literature suggests that the orexin 2 receptor may be
important in the effect of orexin on physical activity (Kotz, 2006);
the lack of availability of a specific orexin 2 receptor antagonist has
hampered this line of investigation.
Of note is that behavioural effects of orexin depend upon the site
of action. In the Kotz laboratory, orexin A injected in almost all
brain areas increased SPA, whereas feeding behaviour was only
influenced after injection into some of these same sites (Kotz et al.,
2008). The time course of action is different for the feeding and
activity effects of orexin A (Thorpe et al., 2003), indicting that the
presence of one behaviour (feeding or SPA) does not depend upon
the other. Thus, the question of displacement – whether one action
of SPA displaces another – is still relevant, but may be less so owing
to the temporal response. Likewise, whether orexin A-induced SPA
is a derivative of enhanced wakefulness is uncertain, but this may
be too simplistic an explanation. One must be awake to exhibit SPA,
and so a sequence of waking, prior to physical activity, would be
advantageous in producing elevated SPA. Narcoleptic humans
lacking orexin are equally as awake as non-narcoleptics, yet are
significantly heavier (Dahmen et al., 2001; Kotagal et al., 2004;
Schuld et al., 2000). This argues against the idea that amount of
time spent awake is directly related to the amount of time in SPA.
In support of this concept, obesity-resistant (OR) rats with elevated
SPA are awake the same amount of time as obesity prone (OP)
animals with reduced SPA (Mavanji et al., 2010).
T. Garland, Jr and others
The standard laboratory rat model – the Sprague Dawley rat – is
subject to variability in weight gain, regardless of diet exposure.
Understanding the difference between rats that remain lean vs those
who become obese (with aging) is the subject of intense investigation
(e.g. Rada et al., 2010). Differences in absolute food intake clearly
contribute to differences in body weight gain, but caloric intake is
not the sole determinant. Studies in the Kotz laboratory and others
suggest that energy expenditure differences, due to SPA, may
underlie phenotypic differences in body weight gain (Levin et al.,
1997; Ricci and Levin, 2003; Teske et al., 2006).
Indirect calorimetry studies show that, on a daily basis, OR and
OP rats expend approximately the same number of absolute
kilojoules (Kotz et al., 2008), in spite of reduced body fat, reduced
energy intake and smaller body size in OR rats. This is important
to note because several factors influence energy expenditure
measurements, including body circumference (heat loss is a function
of total area of the body), amount of body fat and lean mass (adipose
tissue is approximately 20% as metabolically active as lean tissue)
and energy intake differences (energy consumed results in energy
expended, if body mass remains constant). The value of correcting
estimates of energy expenditure for these factors is hotly debated,
and there is not an agreed-upon standard method (Arch et al., 2006;
Kaiyala et al., 2010). Nonetheless, the existing data suggest that
NEAT in OR rats may be an important factor in preventing these
rats from developing obesity (Teske et al., 2006).
Evidence from the Kotz laboratory suggests that OR rats are more
sensitive to the SPA-promoting effects of orexin A, relative to both
Sprague Dawley (Teske et al., 2006) and OP rats (Novak et al.,
2006; Teske et al., 2006), implicating neurobiological differences
in orexin signaling. Potential orexin signaling differences in OP and
OR rats are now being explored, but the existing data show
increased brain gene expression (Teske et al., 2006) and peptide
content of orexin receptors (T. A. Butterick, personal
communication) in OR rats. Together these data suggest that
sensitivity to orexin A SPA promotion is inherent to OR rats and
may be critically important to their obesity resistance. This is
supported primarily by: (1) data showing OR rats remain lean despite
enhanced food intake (on a per body weight basis relative to OP
rats) (Teske et al., 2006), (2) observations in humans that loss of
brain orexin (due to autoimmunity to orexin in narcolepsy) results
in increased body mass index (Kotagal et al., 2004; Schuld et al.,
2000), and (3) in rodents, loss of brain orexin (e.g. through pre- and
post-natal genetic engineering approaches) results in reduced SPA
and increased body weight, despite reduced feeding behaviour (Hara
et al., 2001).
In conclusion, both human and animal studies indicate that SPA,
and the resultant energy expenditure (NEAT) are inherent,
biologically regulated and may protect against obesity. Multiple
neuromodulators exist that interact and respond to environmental
and physiological stimuli to define the level of SPA (and thus
NEAT). These brain networks, which are important to SPA (and
thus NEAT; Fig. 5), are beginning to be defined and may serve as
new therapeutic targets of obesity research.
Personality correlates of physical activity
As discussed by Rhodes and Smith, the study of human personality
“has a long and tumultuous history” with “numerous definitions,
but most encompass the concepts that traits are enduring and
consistent individual-level differences in tendencies to show
consistent patterns of thoughts, feelings and actions” [(Rhodes and
Smith, 2006) p. 958]. Their meta-analysis showed that the amount
of physical activity in humans was positively related to the major
THE JOURNAL OF EXPERIMENTAL BIOLOGY
217 Exercise, spontaneous activity and obesity
personality traits of extraversion and conscientiousness, and
negatively to neuroticism (Rhodes and Smith, 2006). Importantly,
this meta-analysis did not consider sedentary behaviours as a
separate variable, nor did it consider obesity or other indicators of
physical health. However, previous studies (see also Dishman et
al., 2008) have shown that human personality is related to health-
related behaviours (e.g. exercise and maintaining a good diet)
(Booth-Kewley and Vickers, 1994), obesity (Lykouras, 2008; Barry
et al., 2009), and the metabolic syndrome (Sutin et al., 2010).
Donahoo et al. (Donahoo et al., 2004) and Johanssen and Ravussin
(Johanssen and Ravussin, 2008) speculated that individuals with
inherently higher SPA may subconsciously tend to choose jobs that
are more physically demanding. Ebstein reviewed empirical studies
of the molecular genetic architecture of human personality (Ebstein,
In other species, concepts similar to personality are often termed
coping styles, temperaments, behavioural tendencies, strategies,
syndromes, axes or constructs (Gosling, 2001; Sih et al., 2004a;
Koolhaas et al., 2007; Stamps and Groothuis, 2010). For example,
Réale et al. stated that: “Temperament describes the idea that
individual behavioural differences are repeatable over time and
across situations” [(Réale et al., 2007) p. 291]. Of course, studies
of human and non-human personality necessarily entail some major
conceptual and empirical differences. First, human personality is
usually measured using questionnaires that attempt to sample major,
independent axes of behavioural variation in various situations of
modern life. Obviously, such questionnaires cannot be administered
to other animals. Nevertheless, the basic idea remains the same –
a few major traits (or axes of variation in correlated traits) underlie
(or at least predict) an individual’s behaviour in many situations.
Second, human personality traits are generally conceptualized and
studied independent of each other, whereas animal personality traits
are often assumed to be correlated strongly enough that they form
behavioural syndromes. A third major difference is that, in non-
human personality studies, consistently high vs low voluntary
activity in a familiar environment is viewed as a temperament trait
in and of itself, i.e. one of five major temperament trait categories:
shyness–boldness, exploration–avoidance, activity, sociability and
aggressiveness (Réale et al., 2007). In a risky environment, high
activity becomes boldness. In a non-risky but novel environment,
activity is interpreted as exploration (Réale et al., 2007). Some
caution is due when reading the literature on personality of non-
human animals, as this term is sometimes used merely to increase
the appeal of a rather ordinary study of behaviour (e.g. concerning
the repeatability of behaviour or correlations between different
aspects of behaviour). Nonetheless, it has been established that, in
rodents, birds and fish, some individuals are consistently more risk-
taking and exploratory than others, and these aspects consistently
reflect other behaviours, such as foraging style and invasion of novel
territories (see below).
Irrespective of the extent to which personality traits correlate with
the habitual level of physical activity due to some common
underlying mechanisms, physical activity can have strong effects
on cognitive and affective functions (Dey, 1994; Dietrich and
McDaniel, 2004; Ekkekakis et al., 2005; Hillman et al., 2008;
Tomporowski et al., 2008). Daily exercise in humans has been shown
to attenuate anxiety and depression (Kligman and Pepin, 1992;
Antunes et al., 2005) and to stimulate learning capability (Hillman
et al., 2008), and similar effects of voluntary wheel running on
emotionality, stress coping and learning have been demonstrated in
rodents (Greenwood et al., 2007; Pietropaolo et al., 2008; van Praag
et al., 1999; Rhodes et al., 2003b). Although this is certainly
interesting in light of the idea that obese individuals are more
frequently depressed than lean ones (McElroy et al., 2004), it does
not necessarily mean that individuals display a high level of
voluntary activity to ‘subconsciously medicate’ themselves. A
perhaps more probable explanation is that the display of voluntary
activity is a trait characteristic of one’s personality.
In non-human studies, it is recognized that a fundamental factor
structuring personality is the degree to which individual behaviour
is guided by environmental stimuli (Sih et al., 2004; Groothuis and
Carere, 2005; Korte et al., 2005). Individuals can differ along an
axis that ranges from paying close attention to environmental stimuli
and quickly adapting their behaviour to the prevailing conditions
(i.e. flexible, reactive individuals) to those that show more rigid,
routine-like behaviours, regardless of conditions (i.e. proactive
individuals). Relative to reactive animals, proactive animals are often
aggressive to male conspecific rivals, are more impulsive, take
Fig.5. SPA (and resulting NEAT) regulatory brain areas and
associated neuropeptides/transmitters [updated from fig.1 in Kotz
(Kotz, 2008)]. Colors correspond to specific
neuropeptides/hormones as follows: blue, orexin; purple, CCK;
pink, NMU; orange, Agrp; brown, POMC; green, ghrelin; yellow,
leptin. Areas with these colors indicate site of synthesis (e.g.
AgRP, POMC and ARC; orexin, LH), peripheral source (NMU,
ghrelin, leptin and CCK), areas in which the
neuropeptide/hormone has been injected and effects on SPA
reported, or proposed site(s) of action (see text). Signals from all
of these areas have the potential to influence cortical premotor
neurons. Brain areas are not to scale, and connections and
neuropeptides/transmitters indicated are not all-inclusive. Outline
of rat brain was modified from Paxinos and Watson (Paxinos and
Watson, 1990). For an alternative depiction, see fig.2 in
Castaneda et al. (Castaneda et al., 2005). 5HT, serotonin; Agrp,
Agouti-related protein; ARC, hypothalamic arcuate nucleus; CCK,
cholecystokinin; CRH, corticotrophin releasing hormone; DA,
dopamine; LC, locus coeruleus; LH, lateral hypothalamus; MCH,
melanin concentrating hormone; NAccSH, shell of nucleus
accumbens; NE, norepinephrine; NMU, neuromedin U; NPY,
neuropeptide Y; POMC, proopiomelanocortin; PVN, hypothalamic
paraventricular nucleus; VTA, ventral tegmental area; rLH, rostral
LH; SN, substantia nigra; TMN, tuberomammillary nucleus.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
higher risks in the face of potential dangers and are impulsive in
decision-making (Steimer et al., 1999; Steimer et al., 2003;
Groothuis and Carere, 2005). Specific behaviours like these have
been referred to as coping style, and they have been observed in
birds (van Oers et al., 2004; Verbeek et al., 2008), mammals
(Koolhaas et al., 1999) and primates including humans (Aron and
Aron, 1997). To our knowledge, no distinction has been made on
the basis of physical activity (or sedentary behaviours as such), and
it remains to be investigated whether the display of voluntary
exercise and/or SPA aligns with the above-mentioned behavioural
categories. It can nevertheless be hypothesized that proactive
individuals tend to be more agile and physically active (to oppose
environmental challenges) than reactive animals. This idea needs
empirical testing. Recently, rats from lines that vary in coping style
were shown also to vary in responses to a high-fat diet, leading to
the suggestion that “a proactive personality might be protected
against the development of diet-induced insulin resistance”
[(Boersma et al., 2010) p. 401].
Because the expression of proactive-like behaviours (activity,
aggressiveness, boldness) is supposedly energy demanding, Careau
et al. recently suggested the existence of fundamental associations
between personality types and energy expenditure (Careau et al.,
2008). Empirical support for associations between personality and
energetics has just begun to accumulate in fishes (Huntingford et
al., 2010), dogs (Careau et al., 2010) and rodents (Careau et al.,
2009) (but see Jonas et al., 2010b), but how these associations may
relate to SPA and voluntary exercise is unclear.
Because the expression of personality can be strongly contingent
on the environmental context, Careau and colleagues argued that
psychological stress (e.g. as a response to conspecific rivalry, caused
by assessment of locomotor behaviour or energy expenditure) may
lead to more freezing behaviour in passively coping animals, which
would be scored by locomotion detectors as resting (sedentary)
behaviour (Careau et al., 2008). However, whether animals would
freeze for periods long enough to be a problem in practice has not
been established through empirical studies. The converse may also
be true – that some animals with a proactive coping style are trying
to escape from confinement; i.e. they would have a relative
overestimation of energy devoted to SPA, as this activity may have
a large volitional component (see above). However, as noted by
Careau et al. (Careau et al., 2008), many investigators carefully
exclude individuals that do not appear to rest sufficiently – which
may lead to a bias. These possibilities pose an intrinsic difficulty
for assessment of several aspects of DEE under certain conditions,
because animal personality may affect the amount of energy
expended under resting conditions, SPA and volitional activity
(Careau et al., 2008). For this reason, it is useful to investigate in
more detail the behavioural syndrome or personality of animals that
are known to differ in SPA under normal, habituated conditions.
We have previously investigated whether and how certain
personality traits are associated with the level of SPA in mice from
HR lines and their controls (Swallow et al., 1998). If some
personality traits are indeed related to voluntary physical activity,
then one would hypothesize that the expression of homologous
behavioural traits will be altered in HR mice relative to control lines.
Extraversion has homology with exploratory behaviour (Garcia-
Sevilla, 1984), which can be assessed in mice by placing them in
a open field, typically a circular or square arena of approximately
1m diameter. The tendency of the animal to explore the open inner
surface is positively related to the level of exploratory behaviour
(Archer, 1973). At generation 22, Bronikowski et al. did not
observe any statistically significant differences between HR and
T. Garland, Jr and others
control mice for distance traveled, defecation, time spent in the
interior or average distance from the center of the arena during a
3-min trial (Bronikowski et al., 2001). However, HR mice of both
sexes turned less in their travel paths, and HR females had slower
travel times (longer latencies) to reach the wall on trial day 1.
Recently, we re-evaluated this outcome in two of the HR lines and
one control line from a later generation (45) (Jonas et al., 2010b).
One of the two HR lines (line 8) showed more locomotion and visit
frequency towards the middle surface than the controls, and also
showed more exploration in the center when the novel object was
placed in the center of the open field. Another HR line (line 7),
however, did not increase locomotion towards the middle ring, but
instead showed almost a doubling of rearing or upright behaviour
exploring the wall compared with control and line 8 mice. If scored
with automated locomotion detection, this may have lead to
tremendously different interpretations. Furthermore, when a novel
object was placed in the center of the open field, only HR line 8
mice showed increased approach and (presumed) risk-assessment
behaviour (Augustsson et al., 2004). The response in this particular
risk-taking line of mice may be secondary to the horizontal
expression of exploratory behaviour, which is more likely to be
followed by approach behaviour to the novel object than the vertical
expression of exploratory behaviour displayed by line 7 mice. Thus,
although expressed differently, both HR lines clearly showed
increased information-gathering behaviour in the empty open field.
The willingness of mice to explore an open field bears an emotional
stress component. In other words, if the anxiety level is high the
tendency of an animal is to stay away from the open inner
compartment of the open field. One way to test anxiety more directly
in mice is to place them on an elevated plus maze, with two opposing
arms fenced off by high walls, and the other two opposing arms
left open (Lister, 1987). The extent to which animals expose
themselves on the open arms vs the closed arms is correlated with
the level of anxiety (with low open exposure linked to a high level
of anxiety). To our surprise, HR mice spent, on average, less time
in the open arms than the control mice, suggesting that the former
have higher anxiety levels. This seems incongruent with the
aforementioned findings in the surveyable open-field tests, where
HR mice showed more exploratory and risk-taking behaviour (in
line 8). Moreover, it seems incongruent with the overarching
hypothesis that high and (relatively) low voluntarily physically active
mice would fall into the same categories as the proactive and reactive
One interesting aspect of behavioural changes in the HR mice is
increased predatory aggression when non-fasted mice are offered
live crickets, possibly related to alterations in dopamine signaling
(Gammie et al., 2003). This difference does not generalize to other
types of aggression, as HR and control lines show no statistical
differences in intermale or maternal aggression. The positive genetic
correlation between predatory tendencies and the propensity and/or
ability to run long distances on a daily basis could, if it occurs in
wild populations of rodents, facilitate the adaptive evolution of an
active, widely foraging lifestyle (Feder et al., 2010).
An important factor not previously mentioned in this context is
the nutritional status of the animal, which could alter behavioural
responses to environmental stimuli (and perhaps including those in
the elevated plus maze), as well as affect activity levels directly
(see Meek et al., 2010). In rodents, food restriction has been reported
both to alleviate (Inoue et al., 2004; Yamamoto et al., 2009) and to
increase anxiety (Jahng et al., 2007). However, a field study of great
tits found that food availability in the environment was a major
determinant in the differential survival of fast- and slow-exploring
THE JOURNAL OF EXPERIMENTAL BIOLOGY
219 Exercise, spontaneous activity and obesity
birds (Dingemanse et al., 2004). After a sudden drop in food
availability, fast-exploring juvenile tits more rapidly invaded new
territories than slow-exploring individuals (van Overveld et al.,
2010). Although this needs a much more elaborate experimental
approach using carefully characterized animals, it does suggests that
fast-exploring and thus potentially physically active individuals
range over larger territories compared with slow-exploring and thus
potentially inactive ones. As mentioned earlier and suggested by
Careau et al. (Careau et al., 2008), inactive animals may have an
advantage over active animals under high predation pressure if their
frugal energy budget allows them to be less noticeable to predators
(e.g. Clobert et al., 2000). It can be hypothesized that these
alternative strategies belong to different suites of correlated
behaviours (or personalities by some definitions), which have been
shaped by the trade-off between retaining energy (i.e. a thrifty
phenotype) and spending energy anticipating future energy return
(i.e. an exploratory phenotype). It would be of interest to test the
relevance of any of these interactions and potential trade-offs in
mice living in natural habitats (Benus et al., 1991). Recently, a
theoretical basis was provided for the idea that energy trade-offs
can, in fact, rapidly shape different personalities, and can lead to
dimorphic populations (Wolf et al., 2007; Wolf et al., 2008).
A major challenge is to understand correlated behaviours in a
neurobiological context. An attempt can be made based on the
transient hypofrontality hypothesis of Dietrich and colleagues
(Dietrich, 2003; Dietrich and Sparling, 2004). They suggested that
there is a temporary inhibition of neural activity in the prefrontal
lobe during monotonous, automated movements (Dietrich and
McDaniel, 2004). At the same time, when neuronal activity in the
prefrontal lobes becomes reduced, the basal ganglia – which control
routine behaviour – are being activated (Dietrich, 2003; Dietrich
and McDaniel, 2004; Graybiel, 2008). It is possible that mice from
the HR lines have a higher capacity of re-allocating neuronal activity
from prefrontal cortical regions to the basal ganglia during exercise,
which may help them to sustain this behaviour. This could provide
a neurobiological explanation for the observation that HR mice, after
extensive maze learning, visited a former habituated exit in the
complex maze when a new exit is somewhere else (Jonas et al.,
2010b). In a novel environment, however, where the HR mice are
faced with uncertainty (such as in the open field), this switch of
neuronal activity from prefrontal cortex to basal ganglia probably
does not occur. If anything, the HR mice show increased curiosity
and exploration in a novel environment, which reduces uncertainty
and vigilance (Dember and Earl, 1957; Berlyne, 1966). Thus, in
nature, where resources sometimes become temporarily reduced
and/or predation increases, the behavioural characteristics of
increased locomotor activity associated with – or perhaps causal to
– curiosity and exploration may be favored by selection. This
hypothesis could be partially tested by housing of individuals in
semi-natural enclosures (e.g. see Potts et al., 1991; Carroll et al.,
Synthesis; physical activity, neurobiology and personality
Given that both eating and voluntary exercise can be rewarding and
invoke hedonic responses, can be addictive and are affected by some
of the same neurotransmitter systems (e.g. dopamine) and brain
regions (e.g. nucleus accumbens), it would be expected that factors
affecting one may also affect the other (see also Dishman, 2008;
De Chiara et al., 2010). Hence, we may expect genetic variants that
mainly affect, say, appetite or responses to (preferences for) specific
dietary components also to have pleiotropic effects on the propensity
for voluntary exercise. Accordingly, Mathes et al. compared one of
the HR lines of mice, a hyperphagic line that had been bred for
increased body mass and body fat (M16), with a non-selected
Institute of Cancer Research (ICR) line representing the base
population from which both HR and M16 were selected (Mathes
et al., 2010). HPLC analysis showed significant strain effects on
brain neurotransmitter concentrations and microarray analysis
showed significant differences in brain gene expression between
HR and M16 compared with ICR mice in both dorsal striatum and
nucleus accumbens. Results demonstrate that alterations within
central reward pathways can contribute to both obesity and excessive
exercise, and suggest that similar modifications within the dopamine
system may contribute to the expression of opposite phenotypes in
Of course, several other hormones, neuropeptides and
neurotransmitters may be involved in the exerciser phenotype. For
example, leptin affects appetite, energy balance, motivation, reward
and locomotor activity via effects on the central nervous system
(e.g. Girard et al., 2007; Hillebrand et al., 2008; DiLeone, 2009;
Gluckman and Hanson, 2009; Zheng et al., 2009; Davis, 2010; Davis
et al., 2010) and hence could lead to pleiotropic genetic effects. A
recent study indicates that restoration of leptin receptors exclusively
in POMC neurons in hypoactive, leptin-receptor deficient mice
(db/db) enhances locomotor activity, suggesting that POMC
mediates locomotor activity (Huo et al., 2009). Young adult female
HR mice show reduced circulating leptin levels, even after adjusting
statistically for their reduced body fat (Girard et al., 2007). Both
sexes of HR mice show elevated circulating adiponectin (Vaanholt
et al., 2007c; Vaanholt et al., 2008) and corticosterone (Malisch et
al., 2007; Malisch et al., 2008) levels. In children (Metcalf et al.,
2009), physical activity measured by accelerometers (over 7days
and averaged for readings taken at ages of 5, 6, 7 and 8years) showed
a weak negative correlation with circulating adiponectin level
(partial r controlling for sex–0.18, P0.02, N213), a result that
is at odds with findings from the HR lines of mice. In the same
study, activity was not correlated with circulating leptin level
(r0.04), which again seems at odds with the HR mice and with
some studies of adult humans (e.g. Franks et al., 2003). The causality
of such relationships is presently unclear, e.g. because the HR mice
show elevated SPA when housed without wheels (Malisch et al.,
2009). One possibility worth exploring is whether genetically
Voluntary wheel running
Fig.6. Western diet increased voluntary wheel running of mice from High
Runner (HR) lines by ~52% (+36% duration of running, +18% average
speed of running) during days 17–30 of wheel access, whereas it had no
effect on control lines [standard diet (Std)] [data from Meek et al. (Meek et
al., 2010)]. This stimulation of voluntary exercise is remarkable in particular
because the lines have been at an apparent selection limit for ~35
generations (see Kolb et al., 2010). Data are adjusted means + s.e.m.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
elevated levels of adiponectin not only stimulate fat oxidation (e.g.
in active muscles that allow sustainable increases in physical
activity), but also augment SPA or voluntary exercise by actions in
the brain. Although a number of rodent studies have investigated
effects of genetic (e.g. by overexpression) or pharmacological
manipulation of adiponectin on aspects of energy balance (see
reviews below), they apparently have not addressed specific
locomotor effects of these treatments.
Some rodent studies show that high-fat diet can affect (typically
lower) SPA and/or wheel running, although others have not found
an effect [Hesse et al. (Hesse et al., 2010) and references therein].
Brownlow et al. reported no statistical effect of high-fat diets
administered for 10weeks on SPA of male C57BL/6J or A/J mice
measured by photobeams for 1hour during the dark phase
(Brownlow et al., 1996). Cheng et al. reported that male Long–Evans
rats fed high-fat diet for up to 6weeks did not show any statistical
effect on voluntary wheel-running distance compared with rats fed
a standard diet (Cheng et al., 1997). Funkat et al. administered a
high-fat diet to males from three strains of mice for 6weeks, then
measured SPA and wheel running in separate groups of individuals
on days 3–7 of a 7-day test (Funkat et al., 2004). High-fat diet
significantly decreased SPA (in horizontal, vertical and ambulatory
measures) in one of three strains (C57BL/6) whereas it decreased
the vertical component in another strain (129T2). By contrast, wheel
running was significantly decreased in both C57BL/6 and DBA/2
mice. Bjursell et al. also reported that high-fat diet reduced SPA in
male C57Bl/6J mice, both acutely (within 24h) and over a 21-day
period (Bjursell et al., 2008). When housed without wheel access,
both the high capacity runner (HCR) and low capacity runner (LCR)
lines of endurance-selected rats showed a similar decrease in daily
activity levels in response to high-fat feeding for 1month (Novak
et al., 2010). In a study of mice bred for low or high percent body
fat, low-body-fat mice did not change their total wheel-running
distance on being fed a high-fat diet (Simoncic et al., 2008). The
high-body-fat mice fed a high-fat diet increased wheel running
compared with high-body-fat mice fed regular chow, but running
distances did not exceed values of low-body-fat animals on either
diet (see fig. 3 in their paper). A high-fat diet did not appear to
affect habituated SPA of control lines of mice in cages over a 48-
hour measurement period, but tended to increase it in HR lines,
although the effects were not statistically significant (Vaanholt et
T. Garland, Jr and others
al., 2008). Remarkably, a Western diet greatly stimulated wheel
running in HR mice whereas it had little or no effect in control mice
(Fig. 6) (Meek et al., 2010); unfortunately, home-cage activity was
not measured. So far as we are aware, this stimulation of voluntary
exercise by Western diet has not been previously reported in any
rodent or human study [but see Simoncic et al. (Simoncic et al.,
2008) and discussion of that paper (Meek et al., 2010)]. In future
studies, it will be important to determine the neural and/or metabolic
mechanisms underlying this unique effect, as well as possible effects
on NEAT/SPA and sedentary behaviours.
We are not aware of any human studies that have specifically
investigated the effects of dietary intervention (e.g. Western diet)
on the amount of voluntary exercise (Jebb, 2007). Most studies of
dietary intervention either restrict diet or reduce a macro- or
micronutrient (e.g. low fat, low sodium) and focus on weight loss
or change in a risk factor for cardiovascular disease (e.g. blood
pressure, cholesterol level), and perhaps measure physical activity
secondarily. However, in an observational study of young adult men,
habitual high-fat consumers had somewhat lower levels of physical
activity and significantly more periods of sedentary behaviour than
habitual low-fat consumers (Blundell and Cooling, 2000). Thus,
dietary preferences and activity levels may be related.
Genetic basis of variation in physical activity and related
Simonen et al. noted that: “The genetic dissection of complex traits
represents a daunting challenge” [(Simonen et al., 2003b) p. 1358].
As one example, the genetics of obesity per se is currently the subject
of intense research in both human populations and rodent models,
and the more we learn about this topic, the more complicated it
seems (Rankinen et al., 2005; O’Rahilly and Farooqi, 2006;
O’Rahilly and Farooqi, 2008; Pomp et al., 2008; Speakman et al.,
2008; de Krom et al., 2009; Fawcett et al., 2010; Rankinen et al.,
2010; Speakman, 2010). Results to date make it clear that the effects
of physical activity on the human body mass index (BMI) vary with
genotype (Rankinen et al., 2010), and similar results have been
observed for different strains of mice (Nehrenberg et al., 2009a;
Meek et al., 2010). More generally, an individual’s adult obesity
phenotype may be affected by epigenetic effects passed on from
mothers, their in utero and early-life experiences (including diet
and opportunity for exercise), developmental plasticity and their
Table1. Examples of measurement techniques for voluntary exercise and spontaneous physical activity, showing the clear separation of
methods in rodents but not in humans
Humans Laboratory rodents
Spontaneous physical activityPedometers
Passive infrared sensors
Direct observation/video analysis
For a review of the measurement of physical activity in humans, see Westerterp (Westerterp, 2009). Note that both voluntary exercise and spontaneous activity
can be quantified in terms of duration, intensity or total activity (product of duration and intensity). Studies can vary considerably in what is reported, even
when a similar apparatus is used, so great caution needs to be exercised when comparing results from different studies.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
221 Exercise, spontaneous activity and obesity
bacterial complement (Ozanne et al., 2004; Kotz, 2008; Pomp and
Mohlke, 2008; Gluckman and Hanson, 2009; Metges, 2009). We
also have strong evidence for genetic effects on aspects of eating
behaviour in both humans and rodents (Koteja et al., 2003; de Castro,
2004; Rankinen and Bouchard, 2006; de Krom et al., 2009), and
emerging evidence that in rodents and humans the propensity for
physical activity and aspects of eating behaviour can be genetically
related and possibly genetically correlated (Cai et al., 2006; Jonas
et al., 2010a; Kumar et al., 2010). Here, we provide a brief summary
of the genetics of physical activity and related phenotypes, many
of which are related to obesity and/or energy intake. Our focus here
is not on the genetics of personality [e.g. see references in Stamps
and Groothuis (Stamps and Groothuis, 2010)], but, as noted in the
previous section, evidence suggests that personality may be an
important component of weight gain and obesity (e.g. Booth-Kewley
and Vickers, 1994; Lykouras, 2008; Barry et al., 2009; Sutin et al.,
2010). Readers should note that the molecular-genetic analysis of
behavioral traits, especially with mice, is undergoing rapid technical
and conceptual progress (Flint and Mott, 2008).
Before delving into specifics, it is worth emphasizing some
general points about the genetic architecture of quantitative traits,
such as body size, metabolic rate and activity level. A recent review
of humans, mice and Drosophila concluded that most quantitative
traits in all of these species are affected by a large number of genetic
loci, most harboring alleles with small effects on the total phenotypic
variance in a population (Flint and Mackay, 2009). In addition,
pleiotropic genetic effects may be pervasive, and many important
genetic variants have been found to occur outside of genes, likely
in regulatory regions, where they may act by altering gene
A wide range of studies in both humans and rodents has
demonstrated some genetic basis for individual variation in both
exercise propensity and ability (see also Dishman, 2008). In
laboratory mice, strain differences in both voluntary wheel running
and treadmill endurance capacity have been documented, thus
demonstrating significant broad-sense heritability (Ebihara and
Tsuji, 1976; Lightfoot et al., 2001; Lightfoot et al., 2004). Breeding
designs have demonstrated narrow-sense heritability of maximal
oxygen consumption in laboratory strains of house mice (Dohm et
al., 2001; Wone et al., 2009). In quantitative genetics, a response
to selective breeding is generally considered to represent the gold
standard for demonstrating that a trait in a given population has
significant narrow-sense heritability. Studies of rodents have
demonstrated that selective breeding can alter various types of
voluntary physical activity, ranging from behaviour in a novel open-
field arena over 3min to wheel running over 2days, as well as
performance abilities in tests of forced exercise (for reviews, see
Rhodes and Kawecki, 2009; Swallow et al., 2009; Feder et al., 2010;
Zombeck et al., 2011). In addition, lines of mice bred for heat loss
per se also show differences in locomotor activity that contribute
to differences in food intake (Mousel et al., 2001).
The level of voluntary exercise is viewed as a function of both
ability and motivation, with their relative importance likely to vary
among species, individuals, and activities. Further studies of activity-
selected lines of mice (the HR lines) and of rats bred for treadmill
endurance have revealed alterations in brain function that seem to
indicate changes in motivation or propensity to exercise on wheels
(Rhodes et al., 2005; Foley et al., 2006; Morishima et al., 2006;
Belke and Garland, 2007; Keeney et al., 2008; Waters et al., 2008)
(R. P. Waters, R. B. Pringle, G. L. Forster, K. J. Renner, J. L.
Malisch, T.G. Jr and J. G. Swallow, unpublished results). Many
other studies of these same lines demonstrate alterations in exercise
abilities (e.g. Howlett et al., 2009; Meek et al., 2009; Kolb et al.,
2010), including altered plasticity for some traits in the HR lines
of mice (Rhodes et al., 2003b; Swallow et al., 2005; Garland and
Kelly, 2006; Gomes et al., 2009). Therefore, genetic factors are
known to be involved in both motivation and ability to engage in
voluntary wheel running in rodents. Further, the increase in
endurance by lines of mice bred for high voluntary wheel running
(Meek et al., 2009) and the divergence in wheel running of two rat
lines bred for high or low endurance capacity indicate that exercise
propensity and ability are positively genetically correlated in these
rodents. [Based on mouse selection experiments, Nehrenberg et al.
have suggested that physical activity and body composition are
negatively genetically correlated (Nehrenberg et al., 2009a).] Finally,
these same lines show parallel differences in SPA, body size, percent
body fat and circulating leptin levels when housed without wheels
(Girard et al., 2007; Malisch et al., 2009; Meek et al., 2010; Novak
et al., 2009; Novak et al., 2010) (T.G., H.S., M.A.C., T.H.M., L.E.C.,
W.A. and R.C.M., unpublished results), with the HR lines of mice
having very low body fat relative to other mouse strains (Nehrenberg
et al., 2009a) (see also Svenson et al., 2007).
Studies of human populations have demonstrated significant
broad-sense heritabilities for both exercise propensity and ability,
as well as trainability (Cai et al., 2006; Seabra et al., 2008; Teran-
Garcia et al., 2008; Stubbe and de Geus, 2009). The significant
heritability of exercise propensity is consistent with one of
Turkheimer’s laws of behavior genetics, which states “all human
behavioral traits are heritable” (Turkheimer, 2000). The estimated
heritability of physical activity varies considerably (range 18–69%)
because of differences in measurement and expression of physical
activity (e.g. retrospective questionnaire vs accelerometer, sports
participation vs MVPA) and composition of the study population
(e.g. family structure, race) (Stubbe and de Geus, 2009).
Recent genome wide-association studies (GWAS) are beginning
to identify individual genes, or at least chromosomal regions, that
contribute to variation in exercise propensity or ability (e.g. Ways
et al., 2007). Studies in rodents (see also Kumar et al., 2010) have
identified a number of quantitative trait loci (QTL) that account for
a statistically significant fraction of the variation in experimentally
created mapping populations (e.g. Lightfoot et al., 2008), including
populations that originated by crossing an HR line with an inbred
strain (Hartmann et al., 2008; Nehrenberg et al., 2009b; Kelly et
al., 2010). Good et al. have proposed that the helix-loop-helix
transcription factor Nhlh7 affects body weight through control of
physical activity levels via effects on either the motivation or the
ability to exercise (Good et al., 2008).
One interesting finding from Kelly et al. is that some QTL were
associated with the trajectory of running across 6days of wheel
access (Kelly et al., 2010). Moreover, the QTL observed for the
regression slope of the wheel-running traits across days often did
not coincide with locations of the QTL for individual days. This
result suggests that the trajectory of voluntary exercise over multiple
days is partly controlled by different genomic regions than those
controlling the behaviour on individual days, which could have
important implications regarding individual variation in human
adherence to exercise regimens or sports participation (see also
Lightfoot, 2008b). Since then, Leamy et al. analysed QTL for wheel
running over a 21-day period in an F2population created by crossing
two inbred strains (Leamy et al., 2010). They reported that some
QTL affected activity early in the period whereas others affected
activity later in the period, and concluded that: (1) the genetic
architecture of physical activity is more complicated than previously
appreciated and (2) it may change with age, as has been observed
THE JOURNAL OF EXPERIMENTAL BIOLOGY
for other complex traits, such as body weight (see also Morgan et
al., 2003). Also, sex-specific, wheel-running QTL have been
reported by both Nehrenberg et al. (Nehrenberg et al., 2009) and
Leamy et al. (Leamy et al., 2010), reinforcing the complexity of
the underlying genetic architecture. Mechanisms that may underlie
sex differences in the genetic basis of voluntary activity (see also
Garland et al., 2010) include the endocannabinoid system and the
sex hormones (Keeney et al., 2008; Lightfoot, 2008a).
Viggiano (Viggiano, 2008) provides a meta-analysis based on a
database of genetic modifications, brain lesions and pharmacological
interventions that increase locomotor activity in a novel environment
in rodents, i.e. not the same as habituated SPA or voluntary
exercise, which are the foci of the present paper. Nonetheless, the
results are of interest because they may prove to be instructive for
SPA or voluntary exercise. Viggiano estimated that 1.56% of the
genes in the genome are associated with hyperactivity whereas
0.75% result in hypoactivity when altered (Viggiano, 2008); thus,
hundreds of genes are involved. Emphasizing the neurobiological
complexity of hyperactivity, he found that alterations in most
neurotransmitter systems can give rise to a hyperactive phenotype
whereas, perhaps surprisingly, fewer changes are associated with
decreases in locomotor activity. More generally, Viggiano concluded
that there is a net imbalance in the number of altered genes, brain
lesions and/or toxins that can induce hyperactivity vs hypoactivity
(Viggiano, 2008). Importantly, he hypothesized the existence of a
control system that continuously inhibits a basally hyperactive
locomotor tone and that this control system is highly vulnerable to
genetic or environmental effects that occur during prepubertal stages.
Among adult humans, a genome-wide scan based on 432 markers
typed in 767 subjects from 207 Quebec families found ‘promising’
evidence of linkage (P<0.0023) for a measure of physical inactivity
on chromosome 2 (Simonen et al., 2003b). We find it noteworthy
that this measure of what may be termed sedentary behaviour (see
above) showed stronger linkage that any of the three measures of
physical activity (total physical activity, moderate to strenuous
physical activity and time spent in physical activity). There is also
evidence that polymorphisms in the dopamine D2 receptor gene
(Simonen et al., 2003a) and melanocortin-4 receptor gene (Loos et
al., 2005) are associated with physical activity in adults. A QTL on
chromosome 18q contributes to the variation in both physical activity
and dietary intake in children from Hispanic families at high risk
for obesity (Cai et al., 2006). De Moor et al. provide the first genome-
wide association study of physical activity levels, derived from
questionnaires and categorizing individuals simply as exercisers or
nonexercisers, with a prevalence of approximately 50% of exercisers
in both American and Dutch study populations (De Moor et al.,
2009). Although none of the 1.6 million single nucleotide
polymorphisms (SNPs) reached genome-wide significance, SNPs
in three genomic regions showed suggestive significance levels (see
Rankinen et al., 2010). Studies of genetic polymorphisms associated
with performance-related phenotypes (as opposed to measures of
how much they engage in physical activity) are more numerous and
reviewed elsewhere (Teran-Garcia et al., 2008; Rankinen et al.,
Conclusions and future directions
Both voluntary exercise and SPA can be measured in humans and
rodents (Table1), but not all activity fits cleanly into these categories.
Moreover, the comparability of various measures across species is
unclear. Both types of physical activity can have important effects
on energy balance and, hence, on growth rates or body composition
(and reproduction). Over longer time scales, small alterations in
T. Garland, Jr and others
physical activity or energy intake (e.g. ±100kcalday–1) can ‘tip the
scales’, leading to human obesity. Physical activity can aid in
resistance against overweight and obesity (Bi et al., 2005; Bell et
al., 1995; Donahoo et al., 2004; Brock et al., 2005; Hayes et al.,
2008; Levin et al., 2004; Patterson and Levin, 2008), and obese
humans score lower for SPA compared with lean individuals
(Levine, 2007). Aside from this, physical activity has numerous
beneficial effects on various aspects of both mental and physical
health (Booth et al., 2002a,b; Dishman et al., 2006; Booth and Lees,
2007; Cotman et al., 2007; Hillman et al., 2008; Pietropaolo et al.,
2008; Blair and Morris, 2009; Fair and Montgomery, 2009; van
Praag, 2009), including cardiovascular (aerobic) fitness, which may
have independent beneficial effects on health (Hillman et al., 2008;
Church, 2009). Thus, from multiple clinical standpoints, physical
activity should be promoted, particularly for those who live sedentary
lives (Church and Blair, 2009).
Separate from effects of physical activity, a growing number of
human studies indicate that too much or prolonged sitting (sedentary
behaviours) can adversely affect body weight, biomarkers of
metabolic health and other chronic health problems (Owen et al.,
2009; Owen et al., 2010). Given that people in industrialized
societies typically spend the majority of their waking hours engaged
in sedentary behaviours, it has been argued that “reducing sitting
time may have at least as important a role as promoting physical
activity” [(Owen et al., 2009) p. 82]. We are not aware of rodent
studies that have directly investigated sedentary behaviours separate
from measures of voluntary exercise and SPA (Table1) in these
contexts, so this is an important area for future research.
The effects of voluntary exercise on SPA may depend on
duration and intensity, measurement technique, species, sex and age.
Clear evidence that complete compensation often does not occur is
apparent in numerous rodent studies that have shown exercise-
training effects when wheels are provided, e.g. studies on the HR
and control lines of mice (see Dumke et al., 2001; Swallow et al.,
2001; Bronikowski et al., 2002; Rhodes et al., 2003b; Swallow et
al., 2005; Garland and Kelly, 2006; Gomes et al., 2009).
Although wheel access generally increases food consumption in
laboratory rodents, a variety of studies in both rodents and humans
indicate that energy expenditure is not necessarily tightly coupled
to energy intake during relatively short-term exposure. This
mismatch occurs, in part, because eating entails hedonic responses
that can lead to non-homeostatic feeding (Davis et al., 2010). In
addition, certain diets or dietary components are obesogenic beyond
the effect that would be predicted from an elevated digestibility or
caloric content (e.g. Meek et al., 2010). Further, eating can be
affected by societal and cultural factors that often make it much
more than simply a matter of getting enough energy.
As complicated as the control of eating may be, the control of
physical activity is probably at least as complex – and is probably
intertwined mechanistically with the control of eating. In mice, it
has even been shown that certain diets can increase maximal sprint
speed (C. Turbill and T. Ruf, personal communication) or stimulate
voluntary exercise (Meek et al., 2010) and possibly SPA (Vaanholt
et al., 2008) in a genotype-dependent fashion (Simoncic et al., 2008).
Eating and physical activity are related not only through various
biochemical and physiological pathways, but also via brain reward
pathways and, it would seem, they may respond to environmental
stimuli or evolve in non-intuitive ways. For example, it is well
known that pharmaceuticals used to treat ADHD or eating disorders
have a variety of side effects involving, respectively, appetite or
activity. Therefore, it seems likely that effective countermeasures
to encompass the full range of variation in obesity and its causes
THE JOURNAL OF EXPERIMENTAL BIOLOGY
223 Exercise, spontaneous activity and obesity
will need to target feeding behaviour, SPA, voluntary exercise,
sedentary behaviours and perhaps other aspects of personality – or
some combination thereof – depending on the individual (e.g. King
et al., 2007a) and with due recognition of likely sex differences. If
one could predict the extent to which compensation occurs (e.g. the
extent to which prescribed exercise leads to reduced SPA or
increased sedentary and eating behaviour), then one could tailor
treatment of obesity and metabolic derangements more effectively.
Thus, a major challenge lies ahead of us to discover potentially
suitable prospective markers in personality, endocrinology or genetic
List of abbreviations
attention deficit hyperactivity disorder
activity energy expenditure
basal metabolic rate
daily energy expenditure
doubly labeled water
endocannabinoid system; a complex endogenous signaling
system made up of transmembrane cannabinoid receptors,
their ligands (endocannabinoids) and proteins involved in
synthesis and modification of endocannabinoids
field metabolic rate
genome wide-association study
High Runner; four replicate lines of mice that have been bred
for high voluntary wheel running on days 5 and 6 of a 6-
day period of wheel access while they are young adults
(Swallow et al., 1998)
moderate-to-vigorous physical activity, as used in studies of
non-exercise activity thermogenesis
quantitative trait locus
single nucleotide polymorphism
spontaneous physical activity
thermic effect of food
T.G. thanks T. J. Lightfoot, S. A. Kelly and D. Pomp for many helpful discussions
over the years. We thank V. Careau and two anonymous reviewers for comments
on the manuscript. Preparation of this manuscript was supported by US NSF
grants IOB-0543429 (to T.G.) and BCS-0925793 (to L.E.C.) and by NIH/NIDDK
R01 DK078985 (to C.M.K.). Deposited in PMC for release after 12 months.
Ahlenius, S., Hillegaart, V., Thorell, G., Magnusson, O. and Fowler, C. J. (1987).
Suppression of exploratory locomotor activity and increase in dopamine turnover
following the local application of cis-flupenthixol into limbic projection areas of the rat
striatum. Brain Res. 402, 131-138.
Aidman, E. V. and Woollard, S. (2003). The influence of self-reported exercise
addiction on acute emotional and physiological responses to brief exercise
deprivation. Psychol. Sport Exerc. 4, 225-226.
Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S.
J., O’Brien, W. L., Bassett, D. R., Jr, Schmitz, K. H., Emplaincourt, P. O. et al.
(2000). Compendium of physical activities: an update of activity codes and MET
intensities. Med. Sci. Sports Exerc. 32, S498-S504.
Albert, F. W., Carlborg, O., Plyusnina, I., Besnier, F., Hedwig, D., Lautenschlager,
S., Lorenz, D., McIntosh, J., Neumann, C., Richter, H. et al. (2009). Genetic
architecture of tameness in a rat model of animal domestication. Genetics 182, 541-
Allegre, B., Souville, M., Therma, P. and Griffiths M. (2006). Definitions and
measures of exercise dependance. Addict. Res. Theory. 14, 631-646.
Allen, D. L., Harrison, B. C., Maass, A., Bell, M. L., Byrnes, W. C. and Leinwand,
L. A. (2001). Cardiac and skeletal muscle adaptations to voluntary wheel running in
the mouse. J. Appl. Physiol. 90, 1900-1908.
Ang, E. T. and Gomez-Pinilla, F. (2007). Potential therapeutic effects of exercise to
the brain. Curr. Med. Chem. 14, 2564-2571.
Antunes, H. K., Stella, S. G., Santos, R. F., Bueno, O. F. and de Mello, M. T.
(2005). Depression, anxiety and quality of life scores in seniors after an endurance
exercise program. Rev. Bras. Psiquiatr. 27, 266-271.
Arch, J. R., Hislop, D., Wang, S. J. and Speakman, J. R. (2006). Some
mathematical and technical issues in the measurement and interpretation of open-
circuit indirect calorimetry in small animals. Int. J. Obes. (Lond.) 30, 1322-1331.
Archer, J. (1973). Tests for emotionality in rats and mice: a review. Anim. Behav. 21,
Aron, E. N. and Aron, A. (1997). Sensory-processing sensitivity and its relation to
introversion and emotionality. J. Pers. Soc. Psychol. 73, 345-368.
Augustsson, H. and Meyerson, B. J. (2004). Exploration and risk assessment: a
comparative study of male house mice (Mus musculus musculus) and two laboratory
strains. Physiol. Behav. 81, 685-698.
Barry, D., Clarke, M. and Petry, N. M. (2009). Obesity and its relationship to
addictions: is overeating a form of addictive behavior? Am. J. Addict. 18, 439-451.
Belke, T. W. (1996). Investigating the reinforcing properties of running: or, running is
its own reward. In Activity Anorexia: Theory, Research, and Treatment (ed. W. F.
Epling and W. D. Pierce), pp. 45-55. Mahwah, NJ: Lawrence Erlbaum Associates.
Belke, T. W. and Garland, T., Jr (2007). A brief opportunity to run does not function
as a reinforcer for mice selected for high daily wheel-running rates. J. Exp. Anal.
Behav. 88, 199-213.
Belke, T. W. and Heyman, G. M. (1994). A matching law analysis of the reinforcing
efficacy of wheel running in rats. Anim. Learn. Behav. 22, 267-274.
Bell, R. R., Spencer, M. J. and Sherriff, J. L. (1995). Diet-induced obesity in mice
can be treated without energy restriction using exercise and/or a low fat diet. J. Nutr.
Bell, R. R., Spencer, M. J. and Sherriff, J. L. (1997). Voluntary exercise and
monounsaturated canola oil reduce fat gain in mice fed diets high in fat. J. Nutr. 127,
Beltramo, M., de Fonseca, F. R., Navarro, M., Calignano, A., Gorriti, M. A.,
Grammatikopoulos, G., Sadile, A. G., Giuffrida, A. and Piomelli, D. (2000).
Reversal of dopamine D(2) receptor responses by an anandamide transport inhibitor.
J. Neurosci. 20, 3401-3407.
Beninger, R. J. (1983). The role of dopamine in locomotor activity and learning. Brain
Res. 287, 173-196.
Benus, R. F., Bohus, B., Koolhaas, J. M. and van Oortmerssen, G. A. (1991).
Heritable variation for aggression as a reflection of individual coping strategies.
Experientia 47, 1008-1019.
Berlyne, D. E. (1966). Curiosity and exploration. Science 153, 25-33.
Berridge, K. C. and Robinson, T. E. (1998). What is the role of dopamine in reward:
hedonic impact, reward learning, or incentive salience? Brain Res. Rev. 28, 309-369.
Bevan, R., Woakes, A., Butler, P. and Boyd, I. (1994). The use of heart rate to
estimate oxygen consumption of free-ranging black-browed albatrosses (Diomedea
melanophrys). J. Exp. Biol. 193, 119-137.
Bevan, R. M., Speakman, J. R. and Butler, P. J. (1995). Daily energy expenditure of
tufted ducks: a comparison between indirect calorimetry, doubly labelled water, and
heart rate. Funct. Ecol. 9, 40-47.
Bi, S., Scott, K. A., Hyun, J., Ladenheim, E. E. and Moran, T. H. (2005). Running
wheel activity prevents hyperphagia and obesity in Otsuka long-evans Tokushima
Fatty rats: role of hypothalamic signaling. Endocrinology 146, 1676-1685.
Bjursell, M., Gerdin, A. K., Lelliott, C. J., Egecioglu, E., Elmgren, A., Tornell, J.,
Oscarsson, J. and Bohlooly-Y. M. (2008). Acutely reduced locomotor activity is a
major contributor to Western diet-induced obesity in mice. Am. J. Physiol.
Endocrinol. Metab. 294, E251-E260.
Black, A. E. and Cole, T. J. (2000). Within- and between-subject variation in energy
expenditure measured by the doubly-labelled water technique: implications for
validating reported dietary energy intake. Eur. J. Clin. Nutr. 54, 386-394.
Blair, S. N. and Morris, J. N. (2009). Healthy hearts-and the universal benefits of
being physically active: physical activity and health. Ann. Epidemiol. 19, 253-256.
Blum, K., Braverman, E. R., Holder, J. M., Lubar, J. F., Monastra, V. J., Miller, D.,
Lubar, J. O., Chen, T. J. and Comings, D. E. (2000). Reward deficiency syndrome:
a biogenetic model for the diagnosis and treatment of impulsive, addictive, and
compulsive behaviors. J. Psychoactive Drugs 32, 1-112.
Blundell, J. E. (2006). Perspective on the central control of appetite. Obesity 14,
Blundell, J. E. and Cooling, J. (2000). Routes to obesity: phenotypes, food choices
and activity. Br. J. Nutr. 83, S33-S38.
Boersma, G. J., Benthem, L., van Dijk, G., Steimer, T. J. and Scheurink, A. J.
(2010). Coping style predicts the (in)sensitivity for developing hyperinsulinemia on a
high fat diet in rats. Physiol. Behav. 100, 401-407.
Booth, F. W. and Lees, S. J. (2007). Fundamental questions about genes, inactivity,
and chronic diseases. Physiol. Genomics 28, 146-157.
Booth, F. W., Chakravathy, M. V. and Spangenburg, E. E. (2002a). Exercise and
gene expression: physiological regulation of the human genome through physical
activity. J. Physiol. (Lond.) 543, 399-411.
Booth, F. W., Chakravathy, M. U., Gordon, S. E. and Spangenburg, E. E. (2002b).
Waging war on physical inactivity: using modern molecular ammunition against an
ancient enemy. J. Appl. Physiol. 93, 3-30.
Booth-Kewley, S. and Vickers, R. R. (1994). Associations between major domains of
personality and health behavior. J. Pers. 62, 281-298.
Bramble, D. M. and Lieberman, D. E. (2004). Endurance running and the evolution of
Homo. Nature 432, 345-352.
Bray, G. A. (2008). Lifestyle and pharmacological approaches to weight loss: efficacy
and safety. J. Clin. Endocrinol. Metab. 93, S81-S88.
Bray, G. A. and Champagne, C. M. (2005). Beyond energy balance: there is more to
obesity than kilocalories. J. Am. Diet Assoc. 105, S17-S23.
Brené, S., Bjørnebekk, A., Åberg, E., Mathé, A. A., Olson, L. and Werme, M.
(2007). Running is rewarding and antidepressive. Physiol. Behav. 92, 136-140.
Brock, C. M., King, D. S., Wofford, M. R. and Harrell, T. K. (2005). Exercise, insulin
resistance, and hypertension: a complex relationship. Metab. Syndr. Relat. Disord. 3,
Bronikowski, A. M., Carter, P. A., Swallow, J. G., Girard, I. A., Rhodes, J. S. and
Garland, T., Jr (2001). Open-field behavior of house mice selectively bred for high
voluntary wheel running. Behav. Genet. 31, 309-316.
Bronikowski, A. M., Morgan, T. J., Garland, T., Jr and Carter, P. A. (2002).
Antioxidant gene expression in active and sedentary house mice (Mus domesticus)
selected for high voluntary wheel-running behavior. Genetics 161, 1763-1769.
Bronikowski, A. M., Rhodes, J. S., Garland, T., Jr, Prolla, T. A., Awad, T. and
Gammie, S. C. (2004). The evolution of gene expression in the hippocampus in
THE JOURNAL OF EXPERIMENTAL BIOLOGY
224T. Garland, Jr and others
response to selective breeding for increased locomotor activity. Evolution 5, 2079-
Brownlow, B. S., Petro, A., Feinglos, M. N. and Surwit, R. S. (1996). The role of
motor activity in diet-induced obesity in C57BL/6J mice. Physiol. Behav. 60, 37-41.
Burghardt, P. R., Fulk, L. J., Hand, G. A. and Wilson, M. A. (2004). The effects of
chronic treadmill and wheel running on behavior in rats. Brain Res. 1019, 84-96.
Byars, S. G., Ewbank, D., Govindaraju, D. R. and Stearns, S. C. (2010). Colloquium
paper: Natural selection in a contemporary human population. Proc. Natl. Acad. Sci.
USA 107, 1787-1792.
Byers, J. A. and Walker, C. (1995). Refining the motor training hypothesis for the
evolution of play. Am. Nat. 146, 25-40.
Cai, G., Cole, S. A., Butte, N., Bacino, C., Diego, V., Tan, K., Goring, H. H.,
O’Rahilly, S., Farooqi, I. S. and Comuzzie, A. G. (2006). A quantitative trait locus
on chromosome 18q for physical activity and dietary intake in Hispanic children.
Obesity 14, 1596-1604.
Carbone, C., Cowlishaw, G., Isaac, N. J. B. and Rowcliffe, J. M. (2005). How far do
animals go? Determinants of day range in mammals. Am. Nat. 165, 290-297.
Careau, V., Thomas, D., Humphries, M. M. and Reale, D. (2008). Energy
metabolism and animal personality. Oikos 117, 641-653.
Careau, V., Bininda-Emonds, O. R. P., Thomas, D. W., Réale, D. and Humphries,
M. M. (2009). Exploration strategies map along fast-slow metabolic and life-history
continua in muroid rodents. Funct. Ecol. 23, 150-156.
Careau, V., Reale, D., Humphries, M. M. and Thomas, D. W. (2010). The pace of life
under artificial selection: personality, energy expenditure, and longevity are
correlated in domestic dogs. Am. Nat. 175, 753-758.
Carroll, L. S., Meagher, S., Morrison, L., Penn, D. J. and Potts, W. K. (2004).
Fitness effects of a selfish gene (the Mus t complex) are revealed in an ecological
context. Evolution 58, 1318-1328.
Castaneda, T. R., Jürgens, H., Wiedmer, P., Pfluger, P., Diano, S., Horvath, T. L.,
Tang-Christensen, M. and Tschöp, M. H. (2005). Obesity and the neuroendocrine
control of energy homeostasis: the role of spontaneous locomotor activity. J. Nutr.
Chappell, M. A., Shoemaker, V. H., Janes, D. N., Maloney, S. K. and Bucher, T. L.
(1993). Energetics of foraging in breeding Adélie Penguins. Ecology 74, 2450-2461.
Chaput, J. P. and Tremblay, A. (2009). Obesity and physical inactivity: the relevance
of reconsidering the notion of sedentariness. Obesity Facts 2, 249-254.
Cheng, B., Karamizrak, O., Noakes, T. D., Dennis, S. C. and Lambert, E. V. (1997).
Time course of the effects of a high-fat diet and voluntary exercise on muscle
enzyme activity in Long-Evans rats. Physiol. Behav. 61, 701-705.
Christakis, N. A. and Fowler, J. H. (2007). The spread of obesity in a large social
network over 32 years. N. Engl. J. Med. 357, 370-379.
Church, T. S. (2009). The low-fitness phenotype as a risk factor: more than just being
sedentary? Obesity 17, S39-S42.
Church, T. S. and Blair, S. N. (2009). When will we treat physical activity as a
legitimate medical therapy...even though it does not come in a pill? Br. J. Sports.
Med. 43, 80-81.
Clobert, J., Oppliger, A., Sorci, G., Ernande, B., Swallow, J. G. and Garland, T., Jr
(2000). Trade-offs in phenotypic traits: endurance at birth, growth, survival,
predation, and susceptibility to parasitism in a lizard, Lacerta vivipara. Funct. Ecol. 4,
Collier, G. and Hirsch, E. (1971). Reinforcing properties of spontaneous activity in the
rat. J. Comp. Physiol. Psychol. 77, 155-160.
Correa, M., Carlson, B. B., Wisniecki, A. and Salamone, J. D. (2002). Nucleus
accumbens dopamine and work requirements on interval schedules. Behav. Brain
Res. 137, 179-187.
Correa, M., Wisniecki, A., Betz, A., Dobson, D. R., O’Neill, M. F., O’Neill, M. J. and
Salamone, J. D. (2004). The adenosine A2A antagonist KF17837 reverses the
locomotor suppression and tremulous jaw movements induced by haloperidol in rats:
possible relevance to parkinsonism. Behav. Brain Res. 148, 47-54.
Cota, D. and Woods, S. C. (2005). The role of the endocannabinoid system in the
regulation of energy homeostasis. Curr. Opin. Endocrinol. Diabetes 12, 338-351.
Cotman, C. W., Berchtold, N. C. and Christie, L. A. (2007). Exercise builds brain
health: key roles of growth factor cascades and inflammation. Trends Neurosci. 30,
Dahmen, N., Bierbrauer, J. and Kasten, M. (2001). Increased prevalence of obesity
in narcoleptic patients and relatives. Eur. Arch. Psychiatry Clin. Neurosci. 251, 85-
Davis, C., Levitan, R. D., Kaplan, A. S., Carter, J., Reid, C., Curtis, C., Patte, K.,
Hwang, R. and Kennedy, J. L. (2008). Reward sensitivity and the D2 dopamine
receptor gene: a case-control study of binge eating disorder. Prog.
Neuropsychopharmacol. Biol. Psychiatry 32, 620-628.
Davis, C. A., Levitan, R. D., Reid, C., Carter, J. C., Kaplan, A. S., Patte, K. A.,
King, N., Curtis, C. and Kennedy, J. L. (2009). Dopamine for “Wanting” and
opioids for “Liking”: a comparison of obese adults with and without binge eating.
Obesity 17, 1220-1225.
Davis, J. F. (2010). Adipostatic regulation of motivation and emotion. Discov. Med. 9,
Davis, J. F., Choi, D. L. and Benoit, S. C. (2010). Insulin, leptin and reward. Trends
Endocrinol. Metab. 21, 68-74.
De Bono, J. P., Adlam, D., Paterson, D. J. and Channon, K. M. (2006). Novel
quantitative phenotypes of exercise training in mouse models. Am. J. Physiol. Regul.
Integr. Comp. Physiol. 290, R926-R934.
De Castro, J. M. (2004). Genes, the environment and the control of food intake. Br. J.
Nutr. 92, S59-S62.
De Chiara, V., Errico, F., Musella, A., Rossi, S., Mataluni, G., Sacchetti, L.,
Siracusano, A., Castelli, M., Cavasinni, F., Bernardi, G. et al. (2010). Voluntary
exercise and sucrose consumption enhance cannabinoid CB1 receptor sensitivity in
the striatum. Neuropsychopharmacology 35, 374-387.
De Krom, M., Bauer, F., Collier, D., Adan, R. A. H. and la Fleur, S. E. (2009).
Genetic variation and effects on human eating behavior. Annu. Rev. Nutr. 29, 283-
De Lecea, L., Kilduff, T. S., Peyron, C., Gao, X., Foye, P. E., Danielson, P. E.,
Fukuhara, C., Battenberg, E. L., Gautvik, V. T., Bartlett, F. S. et al. (1998). The
hypocretins: hypothalamus-specific peptides with neuroexcitatory activity. Proc. Nat.
Acad. Sci. USA 95, 322-327.
Dember, W. N. and Earl, R. W. (1957). Analysis of exploratory, manipulatory, and
curiosity behaviors. Psychol. Rev. 64, 91-96.
De Moor, M. H., Liu, Y. J., Boomsma, D. I., Li, J., Hamilton, J. J., Hottenga, J. J.,
Levy, S., Liu, X. G., Pei, Y. F., Posthuma, D. et al. (2009). Genome-wide
association study of exercise behavior in Dutch and American adults. Med. Sci.
Sports Exerc. 41, 1887-1895.
Demuth, D. G. and Molleman, A. (2006). Cannabinoid signalling. Life Sci. 78, 549-
De Petrocellis, L., Cascio, M. G. and Di Marzo, V. (2004). The endocannabinoid
system: a general view and latest additions. Br. J. Pharmacol. 141, 765-774.
De Visser, L., van den Bos, R. and Spruijt, B. M. (2005). Automated home cage
observations as a tool to measure the effects of wheel running on cage floor
locomotion. Behav. Brain Res. 160, 382-388.
Dewsbury, D. A. (1980). Wheel-running behavior in 12 species of muroid rodents.
Behav. Process. 5, 271-280.
Dey, S. (1994). Physical exercise as a novel antidepressant agent: possible role of
serotonin receptor subtypes. Physiol. Behav. 55, 323-329.
Dietrich, A. (2003). Functional neuroanatomy of altered states of consciousness: the
transient hypofrontality hypothesis. Conscious. Cogn. 12, 231-256.
Dietrich, A. and McDaniel, W. F. (2004). Endocannabinoids and exercise. Br. J.
Sports Med. 38, 536-541.
Dietrich, A. and Sparling, P. B. (2004). Endurance exercise selectively impairs
prefrontal-dependent cognition. Brain Cogn. 55, 516-524.
DiLeone, R. J. (2009). The influence of leptin on the dopamine system and
implications for ingestive behavior. Int. J. Obes. 33, S25-S29.
Dingemanse, N. J., Both, C., Drent, P. J. and Tinbergen, J. M. (2004). Fitness
consequences of avian personalities in a fluctuating environment. Proc. Biol. Sci.
Dishman, R. K. (1997). Brain monoamines, exercise, and behavioral stress: animal
models. Med. Sci. Sports Exerc. 29, 63-74.
Dishman, R. K. (2008). Gene-physical activity interactions in the etiology of obesity:
behavioral considerations. Obesity 16, S60-S65.
Dishman, R. K., Berthoud, H.-R., Booth, F. W., Cotman, C. W., Edgerton, V. R.,
Fleshner, M. R., Gandevia, S. C., Gomez-Pinilla, F., Greenwood, B. N., Hillman,
C. H. et al. (2006). Neurobiology of exercise. Obesity 14, 345-356.
Dlugosz, E. M., Chappell, M. A., McGillivray, D. G., Syme, D. A. and Garland, T.,
Jr (2009). Locomotor trade-offs in mice selectively bred for high voluntary wheel
running. J. Exp. Biol. 212, 2612-2618.
Dohm, M. R., Hayes, J. P. and Garland, T., Jr (2001). The quantitative genetics of
maximal and basal rates of oxygen consumption in mice. Genetics 159, 267-277.
Donahoo, W. T., Levine, J. A. and Melanson, E. L. (2004). Variability in energy
expenditure and its components. Curr. Opin. Clin. Nutr. Metab. Care 7, 599-605.
Drewett, R. F. (2007). The social facilitation of food intake. Arch. Dis. Child. 92, 377.
Dubreucq, S., Koehl, M., Abrous, D. N., Marsicano, G. and Chaouloff, F. (2010).
CB1 receptor deficiency decreases wheel-running activity: Consequences on
emotional behaviours and hippocampal neurogenesis. Exp. Neurol. 224, 106-113.
Dudley, R. (2001). Limits to human locomotor performance: Phylogenetic origins and
comparative perspectives. J. Exp. Biol. 204, 3235-3240.
Dumke, C. L., Rhodes, J. S., Garland, T., Jr, Maslowski, E., Swallow, J. G., Wetter,
A. C. and Cartee, G. D. (2001). Genetic selection of mice for high voluntary wheel
running: effect on skeletal muscle glucose uptake. J. Appl. Physiol. 9, 1289-1297.
Ebihara, S. and Tsuji, K. (1976). Strain differences in the mouse’s wheel-running
behavior. Jpn. Psychol. Res. 18, 20-29.
Ebstein, R. P. (2006). The molecular genetic architecture of human personality:
beyond self-report questionnaires. Mol. Psychiatry 11, 427-445.
Eikelboom, R. (1999). Human parallel to voluntary wheel running: exercise. Anim.
Behav. 57, F11-F12.
Eikelboom, R. (2001). Bins, bouts and wheel-running speed. Anim. Behav. 61, 679-
Eisenmann, J. C. (2006). Insight into the causes of the recent secular trend in
pediatric obesity: common sense does not always prevail for complex, multi-factorial
phenotypes. Prev. Med. 42, 329-335.
Eisenmann, J. C. and Wickel, E. E. (2009). The biological basis of physical activity in
children: revisited. Ped. Exer. Sci. 21, 257-272.
Eisenmann, J. C., Wickel, E. E., Kelly, S. A., Middleton, K. M. and Garland, T., Jr
(2009). Day-to-day variability in voluntary wheel running among genetically
differentiated lines of mice that vary in activity level. Eur. J. Appl. Physiol. 106, 613-
Ekkekakis, P., Hall, E. E. and Petruzzello, S. J. (2005). Variation and homogeneity in
affective responses to physical activity of varying intensities: an alternative
perspective on dose-response based on evolutionary considerations. J. Sports Sci.
Fahnestock, M., Marchese, M., Head, E., Pop, V., Michalski, B., Milgram, W. N.
and Cotman, C. W. (2010). BDNF increases with behavioral enrichment and an
antioxidant diet in the aged dog. Neurobiol. Aging (in press)
Fair, A. M. and Montgomery, K. (2009). Energy balance, physical activity, and cancer
risk. Methods Mol. Biol. 472, 57-88.
FAO (2004). Human energy requirements. report of a joint FAO/WHO/UNU expert
consultation. Food Nutr. Bull. 26, 166.
Fawcett, G. L., Jarvis, J. P., Roseman, C. C., Wang, B., Wolf, J. B. and Cheverud,
J. M. (2010). Fine-mapping of obesity-related quantitative trait loci in an F9/10
advanced intercross line. Obesity 18, 1383-1392.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
225 Exercise, spontaneous activity and obesity
Feder, M. E., Garland, T., Jr, Marden, J. H. and Zera, A. J. (2010). Locomotion in
response to shifting climate zones: not so fast. Annu. Rev. Physiol. 72,167-190.
Festing, M. F. W. (1977). Wheel activity in 26 strains of mouse. Lab. Anim. 11, 257-
Finkelstein, E. A., Fiebelkorn, I. C. and Wang, G. J. (2003). National medical
spending attributable to overweight and obesity: how much, and who’s paying?
Health Aff. 22, 219-226.
Flint, J. and Mackay, T. F. C. (2009). Genetic architecture of quantitative traits in
mice, flies, and humans. Genome Res. 19, 723-733.
Flint, J. and Mott, R. (2008). Applying mouse complex-trait resources to behavioural
genetics. Nature 456, 724-727.
Foley, T. E., Greenwood, B. N., Day, H. E. W., Koch, L. G., Britton, S. L. and
Fleshner, M. (2006). Elevated central monoamine receptor mRNA in rats bred for
high endurance capacity: implications for central fatigue. Behav. Brain Res. 174,
Ford, E. S., Kohl, H. W., III, Mokdad, A. H. and Ajani, U. A. (2005). Sedentary
behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes.
Res. 13, 608-614.
Franks, P. W., Farooqi, I. S., Luan, J., Wong, M. Y., Halsall, I., O’Rahilly, S. and
Wareham, N. J. (2003). Does physical activity energy expenditure explain the
between-individual variation in plasma leptin concentrations after adjusting for
differences in body composition? J. Clin. Endocrinol. Metab. 88, 3258-3263.
Freed, C. R. and Yamamoto, K. (1985). Regional brain dopamine metabolism: a
marker for the speed, direction, and posture of moving animals. Science 229, 62-65.
Freedson, P. S., Melanson, E. and Sirard, J. (1998). Calibration of the computer
science and applications, inc. accelerometer. Med. Sci. Sports Exerc. 30, 777-781.
Funkat, A., Massa, C. M., Jovanovska, V., Proietto, J. and Andrikopoulos, S.
(2004). Metabolic adaptations of three inbred strains of mice (C57BL6, DBA/2, and
129T2) in response to a high-fat diet. J. Nutr. 134, 3264-3269.
Fuss, J. and Gass, P. (2010). Endocannabinoids and voluntary activity in mice:
Runner’s high and long-term consequences in emotional behaviors. Exp. Neuro.
Gammie, S. C., Hasen, N. S., Rhodes, J. S., Girard, I. and Garland, T., Jr (2003).
Predatory aggression, but not maternal or intermale aggression, is associated with
high voluntary wheel-running behavior in mice. Horm. Behav. 44, 209-221.
Garcia-Sevilla, L. (1984). Extraversion and neuroticism in rats. Pers. Individ. Dif. 5,
Garland, T., Jr (1983). Scaling the ecological cost of transport to body mass in
terrestrial mammals. Am. Nat. 121, 571-587.
Garland, T., Jr (2003). Selection experiments: an under-utilized tool in biomechanics
and organismal biology. In Vertebrate Biomechanics and Evolution (ed. V. L. Bels, J.
P. Gasc and A. Casinos), pp. 23-56. Oxford: BIOS Scientific Publishers.
Garland, T., Jr and Kelly, S. A. (2006). Phenotypic plasticity and experimental
evolution. J. Exp. Biol. 209, 2234-2261.
Garland, T., Jr, Gleeson, T. T., Aronovitz, B. A., Richardson, C. S. and Dohm, M.
R. (1995). Maximal sprint speeds and muscle fiber composition of wild and
laboratory house mice. Physiol. Behav. 58, 869-876.
Garland, T., Jr, Kelly, S. A., Malisch, J. L., Kolb, E. M., Hannon, R. M., Keeney, B.
K., Van Cleave, S. L. and Middleton, K. M. (2010). How to run far: multiple
solutions and sex-specific responses to selective breeding for high voluntary activity
levels. Proc. R. Soc. Lond. B Biol. Sci. (in press) doi:10.1098/rspb.2010.1584.
Gebczynski, A. K. and Konarzewski, M. (2009a). Locomotor activity of mice
divergently selected for basal metabolic rate: a test of hypotheses on the evolution of
endothermy. J. Evol. Biol. 22, 1212-1220.
Gebczynski, A. K. and Konarzewski, M. (2009b). Metabolic correlates of selection on
aerobic capacity in laboratory mice: a test of the model of the evolution of
endothermy. J. Exp. Biol. 212, 2872-2878.
Geiger, B. M., Behr, G. G., Frank, L. E., Caldera-Siu, A. D., Beinfeld, M. C.,
Kokkotou, E. G. and Pothos, E. N. (2008). Evidence for defective mesolimbic
dopamine exocytosis in obesity-prone rats. FASEB J. 22, 2740-2746.
Geiger, B. M., Haburcak, M., Avena, N. M., Moyer, M. C., Hoebel, B. G. and
Pothos, E. N. (2009). Deficits of mesolimbic dopamine neurotransmission in rat
dietary obesity. Neuroscience. 159, 1193-1199.
Girard, I. (2001). Field cost of activity in the kit fox, Vulpes macrotis. Physiol. Biochem.
Zool. 74, 191-202.
Girard, I., McAleer, M. W., Rhodes, J. S. and Garland, T., Jr (2001). Selection for
high voluntary wheel running increases intermittency in house mice (Mus
domesticus). J. Exp. Biol. 204, 4311-4320.
Girard, I., Rezende, E. L. and Garland, T., Jr (2007). Leptin levels and body
composition of mice selectively bred for high voluntary activity. Physiol. Biochem.
Zool. 80, 568-579.
Giuffrida, A., Parsons, L. H., Kerr, T. M., Rodriguez de Fonseca, F., Navarro, M.
and Piomelli, D. (1999). Dopamine activation of endogenous cannabinoid signaling
in dorsal striatum. Nat. Neurosci. 2, 358-363.
Gluckman, P. D. and Hanson, M. A. (2009). Developmental and epigenetic pathways
to obesity: an evolutionary-developmental perspective. Int. J. Obes. 32, S62-S71.
Goldstein, D. L. (1988). Estimates of daily energy expenditure in birds: the time-
energy budget as an integrator of laboratory and field studies. Am. Zool. 28, 829-
Gomes, F. R., Rezende, E. L., Malisch, J. L., Lee, S. K., Rivas, D. A., Kelly, S. A.,
Lytle, C., Yaspelkis, B. B., III and Garland, T., Jr (2009). Glycogen storage and
muscle glucose transporters (GLUT-4) of mice selectively bred for high voluntary
wheel running. J. Exp. Biol. 212, 238-248.
Good, D. J., Coyle, C. A. and Fox, D. L. (2008). Nhlh2: a basic helix-loop-helix
transcription factor controlling physical activity. Exerc. Sport Sci. Rev. 36, 187-192.
Gorman, M. L., Mills, M. G., Raath, J. P. and Speakman, J. R. (1998). High hunting
costs make African wild dogs vulnerable to kleptoparasitism by hyaenas. Nature
Gorriti, M. A., Ferrer, B., del Arco, I., Bermúdez-Silva, F. J., de Diego, Y.,
Fernandez-Espejo, E., Navarro, M. and Rodríguez de Fonseca, F. (2005). Acute
9-tetrahydrocannabinol exposure facilitates quinpirole-induced hyperlocomotion.
Pharmacol. Biochem. Behav. 81, 71-77.
Gosling, S. D. (2001). From mice to men: what can we learn about personality from
animal research. Psychol. Bull. 127, 45-86.
Goszczynski, J. (1986). Locomotor activity of terrestrial predators and its
consequences. Acta Theriol. 31, 79-95.
Graybiel, A. M. (2008). Habits, rituals, and the evaluative brain. Annu. Rev. Neurosci.
Greenwood, B. N., Strong, P. V., Dorey, A. A. and Fleshner, M. (2007). Therapeutic
effects of exercise: wheel running reverses stress-induced interference with shuttle
box escape. Behav. Neurosci. 121, 992-1000.
Groothuis, T. G. and Carere, C. (2005). Avian personalities: characterization and
epigenesis. Neurosci. Biobehav. Rev. 29, 137-150.
Hammond, K. and Diamond, J. (1997). Maximal sustained energy budgets in humans
and animals. Nature 386, 457-462.
Hara, J., Beuckmann, C. T., Nambu, T., Willie, J. T., Chemelli, R. M., Sinton, C. M.,
Sugiyama, F., Yagami, K., Goto, K., Yanagisawa, M. et al. (2001). Genetic
ablation of orexin neurons in mice results in narcolepsy, hypophagia, and obesity.
Neuron 30, 345-354.
Hare, B., Plyusnina, I., Ignacio, N., Schepina, O., Stepika, A., Wrangham, R. and
Trut, L. (2005). Social cognitive evolution in captive foxes is a correlated by-product
of experimental domestication. Curr. Biol. 15, 226-230.
Harri, M., Lindblom, J., Malinen, H., Hyttinen, M., Lapveteläinen, T., Eskola, S. and
Helminen, H. J. (1999). Effect of access to a running wheel on behavior of
C57BL/6J mice. Lab. Anim. Sci. 49, 401-405.
Hart, J. S. (1971). Rodents. In Comparative Physiology of Thermoregulation. Vol. II.
Mammals (ed. G. C. Whittow), pp. 1-149. New York: Academic Press.
Hartmann, J., Garland, T., Jr, Hannon, R. M., Kelly, S. A., Muñoz, G. and Pomp, D.
(2008). Fine mapping of “Mini-Muscle”, a recessive mutation causing reduced hind-
limb muscle mass in mice. J. Hered. 99, 679-687.
Hayes, C. and Kriska, A. (2008). Role of physical activity in diabetes management
and prevention. J. Am. Diet. Assoc. 108, S19-S23.
Healy, G. N., Dunstan, D. W., Salmon, J., Cerin, E., Shaw, J. E., Zimmet, P. Z. and
Owen, N. (2008). Breaks in sedentary time: beneficial associations with metabolic
risk. Diabetes Care 31, 661-666.
Herkenham, M., Lynn, A. B., Little, M. D., Johnson, M. R., Melvin, L. S., de Costa,
B. R. and Rice, K. C. (1990). Cannabinoid receptor localization in brain. Proc. Natl.
Acad. Sci. USA 87, 1932-1936.
Hesse, D., Dunn, M., Heldmaier, G., Klingenspor, M. and Rozman, J. (2010).
Behavioural mechanisms affecting energy regulation in mice prone or resistant to
diet- induced obesity. Physiol. Behav. 99, 370-380.
Hill, J. O., Wyatt, H. R., Reed, G. W. and Peters, J. C. (2003). Obesity and the
environment: where do we go from here? Science 29, 853-855.
Hill, M. N., Titterness, A. K., Morrish, A. C., Carrier, E. J., Lee, T. T., Gil-Mohapel,
J., Gorzalka, B. B., Hillard, C. J. and Christie, B. R. (2010). Endogenous
cannabinoid signaling is required for voluntary exercise-induced enhancement of
progenitor cell proliferation in the hippocampus. Hippocampus 20, 513-523.
Hill, W. F. (1961). Effects of activity deprivation on choice of an activity incentive. J.
Comp. Physiol. Psychol. 54, 78-82.
Hillebrand, J. J., Kas, M. J., van Elburg, A. A., Hoek, H. W. and Adan, R. A.
(2008). Leptin’s effect on hyperactivity: potential downstream effector mechanisms.
Physiol. Behav. 94, 689-695.
Hillman, C. H., Erickson, K. I. and Kramer, A. F. (2008). Be smart, exercise your
heart: exercise effects on brain and cognition. Nat. Rev. Neurosci. 9, 58-65.
Hollowell, R. P., Willis, L. H., Slentz, C. A., Topping, J. D., Bhakpar, M. and Kraus,
W. E. (2009). Effects of exercise training amount on physical activity energy
expenditure. Med. Sci. Sports Exerc. 41, 1640-1644.
Howlett, R. A., Kirkton, S. D., Gonzalez, N. C., Wagner, H. E., Britton, S. L., Koch,
L. G. and Wagner, P. D. (2009). Peripheral oxygen transport and utilization in rats
following continued selective breeding for endurance running capacity. J. Appl.
Physiol. 106, 1819-1825.
Hudson, J. W. and Scott, I. M. (1979). Daily torpor in the laboratory mouse, Mus
musculus var albino. Physiol. Zool. 52, 205-218.
Huntingford, F. A., Andrew, G., Mackenzie, S., Morera, D., Coyle, S. M., Pilarczyk,
M. and Kadri, S. (2010). Coping strategies in a strongly schooling fish, the common
carp Cyprinus carpio. J. Fish Biol. 76, 1576-1591.
Huo, L., Gamber, K., Greeley, S., Silva, J., Huntoon, N., Leng, X. H. and Bjørbaek,
C. (2009). Leptin-dependent control of glucose balance and locomotor activity by
POMC neurons. Cell Metab. 9, 537-547.
Hyman, S. E. and Malenka, R. C. (2001). Addiction and the brain: the neurobiology of
compulsion and its persistence. Nat. Rev. Neurosci. 2, 695-703.
Inoue, K., Zorrilla, E. P., Tabarin, A., Valdez, G. R., Iwasaki, S., Kiriike, N. and
Koob, G. F. (2004). Reduction of anxiety after restricted feeding in the rat:
implication for eating disorders. Biol. Psychiatry 55, 1075-1081.
Iversen, I. H. (1993). Techniques for establishing schedules with wheel running as
reinforcement in rats. J. Exp. Anal. Behav. 60, 219-238.
Jackson, D. M., Djafarian, K., Stewart, J. and Speakman, J. R. (2009). Increased
TV viewing is associated with elevated body fatness but not lower energy
expenditure in pre-school children. Am. J. Clin. Nutr. 89, 1-6.
Jahng, J. W., Kim, J. G., Kim, H. J., Kim, B. T., Kang, D. W. and Lee, J. H. (2007).
Chronic food restriction in young rats results in depression- and anxiety-like
behaviors with decreased expression of serotonin reuptake transporter. Brain Res.
Jebb, S. A. (2007). Dietary determinants of obesity. Obes. Rev. 8, 93-97.
Johannsen, D. L. and Ravussin, E. (2008). Spontaneous physical activity:
relationship between fidgeting and body weight control. Curr. Opin. Endocrinol.
Diabetes Obes. 15, 409-415.
Johnson, P. M. and Kenny, P. J. (2010). Dopamine D2 receptors in addiction-like
reward dysfunction and compulsive eating in obese rats. Nature Neurosci. 13, 635-
THE JOURNAL OF EXPERIMENTAL BIOLOGY
226 T. Garland, Jr and others
Johnson, R. A., Rhodes, J. S., Jeffrey, S. L., Garland, T., Jr and Mitchell, G. S.
(2003). Hippocampal brain-derived neurotrophic factor but not neurotrophin-3
increases more in mice selected for increased voluntary wheel running.
Neuroscience 121, 1-7.
Jonas, I., Vaanholt, L., Doornbos, M., Garland, T., Jr, Scheurink, A. J. W., Nyakas,
C. and van Dijk, G. (2010a). Effects of selective breeding for increased wheel-
running behavior on circadian timing of substrate oxidation and ingestive behavior.
Physiol. Behav. 99, 549-554.
Jonas, I., Schubert, K. A., Reijne, A. C., Scholte, J., Garland, T., Jr, Gerkema, M.
P., Scheurink, A. J. W., Nyakas, C. and van Dijk, G. (2010b). Behavioral traits are
affected by selective breeding for increased wheel-running behavior in mice. Behav.
Genet. 40, 542-550.
Jones, G. H. and Robbins, T. W. (1992). Differential effects of mesocortical,
mesolimbic, and mesostriatal dopamine depletion on spontaneous, conditioned and
drug-induced locomotor activity. Pharmacol. Biochem. Behav. 43, 887-889.
Joosen, A. M., Gielen, M., Vlietinck, R. and Westerterp, K. R. (2005). Genetic
analysis of physical activity in twins. Am. J. Clin. Nutr. 82, 1253-1259.
Jung, A. P. and Luthin, D. R. (2010). Wheel access does not attenuate weight gain in
mice fed high-fat or high-CHO diets. Med. Sci. Sports Exerc. 42, 355-360.
Jung, A. P., Curtis, T. S., Turner, M. J. and Lightfoot, J. T. (2010). Physical activity
and food consumption in high- and low-active inbred mouse strains. Med. Sci.
Sports Exerc. 42, 1826-1833.
Kagan, J. and Berkum, M. (1954). The reward value of running activity. J. Comp.
Physiol. Psychol. 47, 108.
Kaiyala, K. J., Morton, G. J., Leroux, B. G., Ogimoto, K., Wisse, B. and Schwartz,
M. W. (2010). Identification of body fat mass as a major determinant of metabolic
rate in mice. Diabetes 59, 1657-1666.
Kanarek, R. B., D’Anci, K. E., Jurdak, N. and Mathes, W. F. (2009). Running and
addiction: precipitated withdrawal in a rat model of activity-based anorexia. Behav.
Neurosci. 123, 905-912.
Kane, S. L., Garland, T., Jr and Carter, P. A. (2008). Basal metabolic rate of aged
mice is affected by random genetic drift but not by selective breeding for high early-
age locomotor activity or chronic wheel access. Physiol. Biochem. Zool. 81, 288-300.
Kas, M. J., Kaye, W. H., Mathes, W. F. and Bulik, C. M. (2009). Interspecies
genetics of eating disorder traits. Am. J. Med. Genet. B. Neuropsychiatr. Genet.
Keeney, B. K., Raichlen, D. A., Meek, T. H., Wijeratne, R. S., Middleton, K. M.,
Gerdeman, G. L. and Garland, T., Jr. (2008). Differential response to a selective
cannabinoid receptor antagonist (SR141716: rimonabant) in female mice from lines
selectively bred for high voluntary wheel-running behavior. Behav. Pharmacol. 19,
Kelly, S. A., Czech, P. P., Wight, J. T., Blank, K. M. and Garland, T., Jr (2006).
Experimental evolution and phenotypic plasticity of hindlimb bones in high-activity
house mice. J. Morphol. 267, 360-374.
Kelly, S. A., Nehrenberg, D. L., Peirce, J. L., Hua, K., Steffy, B. M., Wiltshire, T.,
Pardo Manuel de Villena, F., Garland, T., Jr and Pomp, D. (2010). Genetic
architecture of voluntary exercise in an advanced intercross line of mice. Physiol.
Genomics 42, 190-200.
Keys, A., Brozek, J., Henschel, A., Mickelson, O. and Anderson, B. L. (1950). The
Biology of Human Starvation. Minneapolis, Minnesota: University of Minnesota
King, N. A., Hopkins, M., Caudwell, P., Stubbs, R. J. and Blundell, J. E. (2007a).
Individual variability following 12 weeks of supervised exercise: identification and
characterization of compensation for exercise-induced weight loss. Int. J. Obes. 32,
King, N. A., Caudwell, P., Hopkins, M., Byrne, N. M., Colley, R., Hills, A. P.,
Stubbs, J. R. and Blundell, J. E. (2007b). Metabolic and behavioral compensatory
responses to exercise interventions: barriers to weight loss. Obesity 15, 1373-1383.
Kiwaki, K., Kotz, C. M., Wang, C., Lanningham-Foster, L. and Levine, J. A. (2004).
Orexin A (hypocretin 1) injected into hypothalamic paraventricular nucleus and
spontaneous physical activity in rats. Am. J. Physiol. Endocrinol. Metab. 286, E551-
Kligman, E. W. and Pepin, E. (1992). Prescribing physical-activity for older patients.
Geriatrics 47, 33-47.
Knab, A. M. and Lightfoot, J. T. (2010). Does the difference between physically
active and couch potato lie in the dopamine system? Int. J. Biol. Sci. 6, 133-150.
Knab, A. M., Bowen, R. S., Hamilton, A. T., Gulledge, A. A. and Lightfoot, J. T.
(2009). Altered dopaminergic profiles: implications for the regulation of voluntary
physical activity. Behav. Brain Res. 204, 147-152.
Kolb, E. M., Kelly, S. A., Middleton, K. M., Sermsakdi, L. S., Chappell, M. A. and
Garland, T., Jr (2010). Erythropoietin elevates VO2,maxbut not voluntary wheel
running in mice. J. Exp. Biol. 213, 510-519.
Koolhaas, J. M., Korte, S. M., De Boer, S. F., van der Vegt, B. J., Van Reenen, C.
G., Hopster, H., De Jon, I. C., Ruis, M. A. W. and Blokhuis, H. J. (1999). Coping
styles in animals: current status in behavior and stress-physiology. Neurosci.
Biobehav. Rev. 23, 925-935.
Koolhaas, J. M., De Boer, S. F., Buwalda, B. and Van Reenen, K. (2007). Individual
variation in coping with stress: a multidimensional approach of ultimate and
proximate mechanisms. Brain Behav. Evol. 70, 218-226.
Korte, S. M., Koolhaas, J. M., Wingfield, J. C. and McEwen, B. S. (2005). The
Darwinian concept of stress: benefits of allostasis and costs of allostatic load and the
trade-offs in health and disease. Neurosci. Biobehav. Rev. 29, 3-38.
Kotagal, S., Krahn, L. E. and Slocumb, N. (2004). A putative link between childhood
narcolepsy and obesity. Sleep Med. 5, 147-150.
Koteja, P. and Garland, T., Jr (2001). Forum: response to R. Eikelboom. Anim.
Behav. 61, F25-F26.
Koteja, P., Garland, T., Jr, Sax, J. K., Swallow, J. G. and Carter, P. A. (1999a).
Behaviour of house mice artificially selected for high levels of voluntary wheel
running. Anim. Behav. 58, 1307-1318.
Koteja, P., Swallow, J. G., Carter, P. A. and Garland, T., Jr (1999b). Energy cost of
wheel running in house mice: implications for coadaptation of locomotion and energy
budgets. Physiol. Biochem. Zool. 72, 238-249.
Koteja, P., Carter, P. A., Swallow, J. G. and Garland, T., Jr (2003). Food wasting in
house mice: variation among individuals, families, and genetic lines. Physiol. Behav.
Kotz, C. M. (2006). Integration of feeding and spontaneous physical activity: role for
orexin. Physiol. Behav. 88, 294-301.
Kotz, C. M. (2008). Rewired to be thin? When exercise hits the brain. Am. J. Physiol.
Regul. Integr. Comp. Physiol. 294, R288-R289.
Kotz, C. M., Teske, J. A., Levine, J. A. and Wang, C. (2002). Feeding and activity
induced by orexin A in the lateral hypothalamus in rats. Regul. Pept. 104, 27-32.
Kotz, C. M., Wang, C., Teske, J. A., Thorpe, A. J., Novak, C. M., Kiwaki, K. and
Levine, J. A. (2006). Orexin A mediation of time spent moving in rats: neural
mechanisms. Neuroscience 142, 29-36.
Kotz, C. M., Teske, J. A. and Billington, C. J. (2008). Neuroregulation of nonexercise
activity thermogenesis and obesity resistance. Am. J. Physiol. Regul. Integr. Comp.
Physiol. 294, R699-R710.
Kumar, K. G., DiCarlo, L. M., Volaufova, J., Zuberi, A. R. and Smith Richards, B.
K. (2010). Increased physical activity cosegregates with higher intake of
carbohydrate and total calories in a subcongenic mouse strain. Mamm. Genome 21,
Lachmansingh, E. and Rollo, C. D. (1994). Evidence for a trade-off between growth
and behavioural activity in giant “Supermice” genetically engineered with extra
growth hormone genes. Can. J. Zool. 72, 2158-2168.
Lacy, R. C. and Lynch, C. B. (1979). Quantitative genetic analysis of temperature
regulation in Mus musculus. I. Partitioning of variance. Genetics 91, 743-753.
Lambert, M. I., van Zyl, C., Jaunky, R., Lambert, E. V. and Noakes, T. D. (1996).
Tests of running performance do not predict subsequent spontaneous running in
rats. Physiol. Behav. 60, 171-176.
Laviolette, S. R. and Grace, A. A. (2006). The roles of cannabinoid and dopamine
receptor systems in emotional learning circuits: implications for schizophrenia and
addiction. Cell. Mol. Life Sci. 63, 1597-1613.
Leamy, L. J., Pomp, D. and Lightfoot, J. T. (2010). A search for quantitative trait loci
controlling within-individual variation of physical activity traits in mice. BMC Genet.
Leasure, J. L. and Jones, M. (2008). Forced and voluntary exercise differentially
affect brain and behavior. Neuroscience 156, 456-465.
Ledwidge, B. (1980). Run for your mind: Aerobic exercise as a means of alleviating
anxiety and depression. Can. J. Behav. Sci./Rev. Can. Sci. Comp. 12, 127.
Leibel, R. L., Rosenbaum, M. and Hirsch, J. (1995). Changes in energy expenditure
resulting from altered body weight. N. Engl. J. Med. 332, 621-628.
Lett, B. T., Grant, V. L., Byrne, M. J. and Koh, M. T. (2000). Pairings of a distinctive
chamber with the after effect of wheel running produce conditioned place preference.
Appetite 34, 87-94.
Levin, B. E. and Dunn-Meynell, A. A. (2004). Chronic exercise lowers the defended
body weight gain and adiposity in diet-induced obese rats. Am. J. Physiol. Regul.
Integr. Comp. Physiol. 286, R771-R778.
Levin, B. E., Dunn-Meynell, A. A., Balkan, B. and Keesey, R. E. (1997). Selective
breeding for diet-induced obesity and resistance in Sprague-Dawley rats. Am. J.
Physiol. 273, R725-R730.
Levine, J. A. (2007). Nonexercise activity thermogenesis-liberating the life-force. J.
Intern. Med. 262, 273-287.
Levine, J. A. and Kotz, C. M. (2005). NEAT - non-exercise activity thermogenesis –
egocentric and geocentric environmental factors vs. biological regulation. Acta
Physiol. Scand. 184, 309-318.
Levine, J. A., Eberhardt, N. L. and Jensen, M. D. (1999). Role of nonexercise
activity thermogenesis in resistance to fat gain in humans. Science 283, 212-214.
Levine, J. A., Nygren, J., Short, K. R. and Nair, K. S. (2003). Effect of
hyperthyroidism on spontaneous physical activity and energy expenditure in rats. J.
Appl. Physiol. 94, 165-170.
Levine, J. A., Lanningham-Foster, L. M., McCrady, S. K., Krizan, A. C., Olson, L.
R., Kane, P. H., Jensen, M. D. and Clark, M. M. (2005). Interindividual variation in
posture allocation: possible role in human obesity. Science 307, 584-586.
Li, G., Rhodes, J. S., Girard, I., Gammie, S. C. and Garland, T., Jr (2004). Opioid-
mediated pain sensitivity in mice bred for high voluntary wheel running. Physiol.
Behav. 83, 515-524.
Lightfoot, J. T. (2008a). Sex hormones’ regulation of rodent physical activity: a review.
Int. J. Biol. Sci. 4, 126-132.
Lightfoot, J. T. (2008b). Commentary on Viewpoint: Perspective on the future use of
genomics in exercise. J. Appl. Physiol. 104, 1249.
Lightfoot, J. T., Turner, M. J., DeBate, K. A. and Kleeberger, S. R. (2001).
Interstrain variation in murine aerobic capacity. Med. Sci. Sports Exerc. 33, 2053-
Lightfoot, J. T., Turner, M. J., Daves, M., Vordermark, A. and Kleeberger, S. R.
(2004). Genetic influence on daily wheel running activity level. Physiol. Genomics 19,
Lightfoot, J. T., Turner, M. J., Pomp, D., Kleeberger, S. R. and Leamy, L. J. (2008).
Quantitative trait loci for physical activity traits in mice. Physiol. Genomics 32, 401-
Lister, R. G. (1987). The use of a plus-maze to measure anxiety in the mouse.
Psychopharmacology (Berl.) 92, 180-185.
Liu, Y., von Deneen, K. M., Kobeissy, F. H. and Gold, M. S. (2010). Food addiction
and obesity: evidence from bench to bedside. J. Psychoactive Drugs 42, 133-145.
Long, B. C. (1983). Aerobic conditioning and stress reduction: participation or
conditioning? Hum. Mov. Sci. 2, 171-186.
Loos, R. J. F., Rankinen, T., Tremblay, A., Perusse, L., Chagnon, Y. and
Bouchard, C. (2005). Melanocortin-4 receptor gene and physical activity in the
Quebec Family Study. Int. J. Obes. 29, 420-428.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
227 Exercise, spontaneous activity and obesity
Lupica, C. R. and Riegel, A. C. (2005). Endocannabinoid release from midbrain
dopamine neurons: a potential substrate for cannabinoid receptor antagonist
treatment of addiction. Neuropharmacology 48, 1105-1116.
Lykouras, L. (2008). Psychological profile of obese patients. Dig. Dis. 26, 36-39.
MacLaren, V. V. and Best, L. A. (2010). Multiple addictive behaviors in young adults:
student norms for the Shorter PROMIS Questionnaire. Addict. Behav. 35, 252-255.
Maldonado, R., Valverde, O. and Berrendero, F. (2006). Involvement of the
endocannabinoid system in drug addiction. Trends Neurosci. 29, 225-232.
Malisch, J. L., Saltzman, W., Gomes, F. R., Rezende, E. L., Jeske, D. R. and
Garland, T., Jr (2007). Baseline and stress-induced plasma corticosterone
concentrations of mice selectively bred for high voluntary wheel running. Physiol.
Biochem. Zool. 80, 146-156.
Malisch, J. L., Breuner, C. W., Gomes, F. R., Chappell, M. A. and Garland, T., Jr
(2008). Circadian pattern of total and free corticosterone concentrations,
corticosteroid-binding globulin, and physical activity in mice selectively bred for high
voluntary wheel-running behavior. Gen. Comp. Endocrinol. 156, 210-217.
Malisch, J. L., Breuner, C. W., Kolb, E. M., Wada, H., Hannon, R. M., Chappell, M.
A., Middleton, K. M. and Garland, T., Jr (2009). Behavioral despair and home-
cage activity in mice with chronically elevated baseline corticosterone concentrations.
Behav. Genet. 39, 192-201.
Mathes, W. F., Nehrenberg, D. L., Gordon, R., Hua, K., Garland, K., Jr and Pomp,
D. (2010). Dopaminergic dysregulation in mice selectively bred for excessive
exercise or obesity. Behav. Brain Res. 210, 155-163.
Matsuda, L. A., Lolait, S. J., Brownstein, B. J., Young, A. C. and Bonner, T. I.
(1990). Structure of a cannabinoid receptor and functional expression of the cloned
cDNA. Nature356, 561-564.
Mavanji, V., Teske, J. A., Billington, C. J. and Kotz, C. M. (2010). Elevated sleep
quality and orexin receptor mRNA in obesity-resistant rats. Int. J. Obes. (Lond.) 34,
McAllister, E. J., Dhurandhar, N. V., Keith, S. W., Aronne, L. J., Barger, J., Baskin,
M., Benca, R. M., Biggio, J., Boggiano, M. M., Eisenmann, J. C. et al. (2009).
Ten putative contributors to the obesity epidemic. Crit. Rev. Food Sci. Nutr. 49, 868-
McDonald, J. M., Ramsey, J. J., Miner, J. L. and Nielsen, M. K. (2009). Differences
in mitochondrial efficiency between lines of mice divergently selected for heat loss. J.
Anim. Sci. 87, 3105-3113.
McElroy, S. L., Kotwal, R., Malhotra, S., Nelson, E. B., Keck, P. E. and Nemeroff,
C. B. (2004). Are mood disorders and obesity related? A review for the mental
health professional. J. Clin. Psychiatry 65, 634-651.
Meek, T. H., Lonquich, B. P., Hannon, R. M. and Garland, T., Jr (2009). Endurance
capacity of mice selectively bred for high voluntary wheel running. J. Exp. Biol. 212,
Meek, T. H., Eisenmann, J. C. and Garland, T., Jr (2010). Western diet increases
wheel running in mice selectively bred for high voluntary wheel running. Int. J. Obes.
Meeusen, R., Smolders, I., Sarre, S., de Meirleir, K., Keizer, H., Serneels, M.,
Ebinger, G. and Michotte, Y. (1997). Endurance training effects on neurotransmitter
release in rat striatum: an in vivo microdialysis study. Acta Physiol. Scand. 159, 335-
Meijer, E. P., Westerterp, K. R. and Verstappen, F. T. (1999). Effect of exercise
training on total daily physical activity in elderly humans. Eur. J. Appl. Physiol.
Occup. Physiol. 80, 16-21.
Meijer, G. A., Janssen, G. M., Westerterp, K. R., Verhoeven, F., Saris, W. H. and
ten Hoor, F. (1991). The effect of a 5-month endurance-training programme on
physical activity: evidence for a sex-difference in the metabolic response to exercise.
Eur. J. Appl. Physiol. Occup. Physiol. 62, 11-17.
Melzer, K., Kayser, B., Saris, W. H. and Pichard, C. (2005). Effects of physical
activity on food intake. Clin. Nutr. 24, 885-895.
Metcalf, B. S., Jeffery, A. N., Hosking, J., Voss, L. D., Sattar, N. and Wilkin, T. J.
(2009). Objectively measured physical activity and its association with adiponectin
and other novel metabolic markers: a longitudinal study in children (EarlyBird 38).
Diabetes Care 32, 468-473.
Metges, C. C. (2009). Early nutrition and later obesity: animal models provide insights
into mechanisms. In Early Nutrition Programming and Health Outcomes in Later Life:
Obesity and Beyond (ed. B. Koletzko, T. Decsi, D. Molnár, and A. De la Hunty), pp.
105-112. Springer Science+Business Media B.V.
Miller, W. C., Koceja, D. M. and Hamilton, E. J. (1997). A meta-analysis of the past
25 years of weight loss research using diet, exercise or diet plus exercise
intervention. Int. J. Obes. Relat. Metab. Disord. 21, 941-947.
Morgan, W. P. (1985). Affective beneficience of vigorous physical activity. Med. Sci.
Sports Exerc. 17, 94-105.
Morgan, T. J., Garland, T., Jr and Carter, P. A. (2003). Ontogenies in mice selected
for high voluntary wheel-running activity. I. Mean ontogenies. Evolution 57, 646-657.
Morishima, M., Harada, N., Hara, S., Sano, A., Seno, H., Takahashi, A., Morita, Y.
and Nakaya, Y. (2006). Monoamine oxidase A activity and norepinephrine level in
hippocampus determine hyperwheel running in SPORTS rats.
Neuropsychopharmacology 31, 2627-2638.
Mousel, M. R., Stroup, W. W. and Nielsen, M. K. (2001). Locomotor activity, core
body temperature, and circadian rhythms in mice selected for high or low heat loss.
J. Anim. Sci. 79, 861-868.
Nagy, K. A. (2001). Food requirements of wild animals: predictive equations for free-
living mammals, reptiles, and birds. Nutr. Abstr. Rev. B 71, 21R-31R.
Nagy, K. A. (2005). Field metabolic rate and body size. J. Exp. Biol. 208, 1621-1625.
Nagy, K. A., Girard, I. A. and Brown, T. K. (1999). Energetics of free-ranging
mammals, reptiles, and birds. Annu. Rev. Nutr. 19, 247-277.
Naylor, A. S., Persson, A. I., Eriksson, P. S., Jonsdottir, I. H. and Thorlin, T.
(2005). Extended voluntary running inhibits exercise-induced adult hippocampal
progenitor proliferation in the spontaneously hypertensive rat. J. Neurophysiol. 93,
Nehrenberg, D. L., Hua, K., Estrada-Smith, D., Garland, T., Jr and Pomp, D.
(2009a). Voluntary exercise and its effects on body composition depend on genetic
selection history. Obesity 17, 1402-1409.
Nehrenberg, D. L., Wang, S., Hannon, R. M., Garland, T., Jr and Pomp, D. (2009b).
QTL underlying voluntary exercise in mice: interactions with the “mini muscle” locus
and sex. J. Hered. 101, 42-53.
NHS (2008). A call to debate: a call to action. A report on the health of the population
of NHS Greater Glasgow and Clyde 2007–2008, 93 pp. Glasgow: NHS Greater
Glasgow and Clyde. http://www.nhsggc.org.uk/content/
Novak, C. M. and Levine, J. A. (2007). Central neural and endocrine mechanisms of
non-exercise activity thermogenesis and their potential impact on obesity. J.
Neuroendocrinol. 19, 923-940.
Novak, C. M., Kotz, C. M. and Levine, J. A. (2006). Central orexin sensitivity,
physical activity, and obesity in diet-induced obese and diet-resistant rats. Am. J.
Physiol. Endocrinol. Metab. 290, E396-E403.
Novak, C. M., Escande, C., Gerber, S. M., Chini, E. N., Zhang, M., Britton, S. L.,
Koch, L. G. and Levine, J. A. (2009). Endurance capacity, not body size,
determines physical activity levels: role of skeletal muscle PEPCK. PLoS ONE 4,
Novak, C. M., Escande, C., Burghardt, P. R., Zhang, M., Barbosa, M. T., Chini, E.
N., Britton, S. L., Koch, L. G., Akil, H. and Levine, J. A. (2010). Spontaneous
activity, economy of activity, and resistance to diet-induced obesity in rats bred for
high intrinsic aerobic capacity. Horm. Behav. 58, 355-367.
Nydon, J. and Thomas, R. B. (1989). Methodological procedures for analysing energy
expenditure. In Research Methods in Nutritional Anthropology (ed. G. H. Pelto, P. J.
Pelto and E. Messer), 217 pp. Tokyo, Japan: United Nations University Press.
Olshansky, S. J., Passaro, D. J., Hershow, R. C., Layden, J., Carnes, B. A., Brody,
J., Hayflick, L., Butler, R. N., Allison, D. B. and Ludwig, D. S. (2005). A potential
decline in life expectancy in the United States in the 21st century. N. Engl. J. Med.
O’Rahilly, S. and Farooqi, I. S. (2006). Genetics of obesity. Philos. Trans. R. Soc.
Lond. B Biol. Sci. 361, 1095-1105.
O’Rahilly, S. and Farooqi, I. S. (2008). Human obesity as a heritable disorder of the
central control of energy balance. Int. J. Obes. 32, S55-S61.
Owen, N., Bauman, A. and Brown, W. (2009). Too much sitting: a novel and
important predictor of chronic disease risk? Br. J. Sports Med. 43, 81-84.
Owen, N., Healy, G. N., Matthews, C. E. and Dunstan, D. W. (2010). Too much
sitting: the population health science of sedentary behavior. Exerc. Sport Sci. Rev.
Owesson-White, C. A., Cheer, J. F., Beyene, M., Carelli, R. M. and Wightman, R.
M. (2008). Dynamic changes in accumbens dopamine correlate with learning during
intracranial self-stimulation. Proc. Natl. Acad. Sci. USA 105, 11957-11962.
Ozanne, S. E., Lewis, R., Jennings, B. J. and Hales, C. N. (2004). Early
programming of weight gain in mice prevents the induction of obesity by a highly
palatable diet. Clin. Sci. 106, 141-145.
Pagotto, U., Cervino, C., Vicennati, V., Marsicano, G., Lutz, B. and Pasquali, R.
(2006). How many sites of action for endocannabinoids to control energy
metabolism? Int. J. Obesity 30, S39-S43.
Patterson, C. M. and Levin, B. E. (2008). Role of exercise in the central regulation of
energy homeostasis and in the prevention of obesity. Neuroendocrinology 87, 65-70.
Paxinos, G. and Watson, C. (1990). The Rat Brain in Stereotaxic Coordinates. New
York: Academic Press.
Pietropaolo, S., Sun, Y., Li, R., Brana, C., Feldon, J. and Yee, B. K. (2008). The
impact of voluntary exercise on mental health in rodents: a neuroplasticity
perspective. Behav. Brain Res. 192, 42-60.
Pillolla, G., Melis, M., Perra, S., Muntoni, A. L., Gessa, G. L. and Pistis, M. (2007).
Medial forebrain bundle stimulation evokes endocannabinoid-mediated modulation of
ventral tegmental area dopamine neuron firing in vivo. Psychopharmacology 191,
Pomp, D. and Mohlke, K. L. (2008). Obesity genes: so close and yet so far... J. Biol.
Pomp, D., Nehrenberg, D. and Estrada-Smith, D. (2008). Complex genetics of
obesity in mouse models. Annu. Rev. Nutr. 28, 331-345.
Potts, W. K., Manning, C. J. and Wakeland, E. K. (1991). Mating patterns in
seminatural populations of mice influenced by MHC genotype. Nature 352, 619-621.
Premack, D., Schaeffer, R. W. and Hundt, A. (1964). Reinforcement of drinking by
running: effect of fixed ratio and reinforcement time. J. Exp. Anal. Behav. 7, 91-96.
Prentice, A. and Jebb, S. (2004). Energy intake/physical activity interactions in the
homeostasis of body weight regulation. Nutr. Rev. 62, S98-S104.
Prentice, A. and Jebb, S. (2006). TV and inactivity are separate contributors to
metabolic risk factors in children. PLoS Med. 3. e481.
Rada, P., Bocarsly, M. E., Barson, J. R., Hoebel, B. G. and Leibowitz, S. F. (2010).
Reduced accumbens dopamine in Sprague-Dawley rats prone to overeating a fat-
rich diet. Physiol. Behav. 101, 394-400.
Raichlen, D. A., Giuffrida, A., Gerdeman, G. L. and Foster, A. D. (2010).
Neurobiological rewards and the evolution of the runner’s high in humans and
cursorial mammals. AAPA annual meeting, April, 2010. Abstract in Am. J. Phys.
Anthropol. 50, 238.
Rankinen, T. and Bouchard, C. (2006). Genetics of food intake and eating behavior
phenotypes in humans. Annu. Rev. Nutr. 26, 413-434.
Rankinen, T., Zuberi, A., Chagnon, Y. C., Weisnagel, S. J., Argyropoulos, G.,
Walts, B., Perusse, L. and Bouchard, C. (2005). The human obesity gene map:
the 2005 update. Obesity 14, 529-644.
Rankinen, T., Roth, S. M., Bray, M. S., Loos, R., Perusse, L., Wolfarth, B.,
Hagberg, J. M. and Bouchard, C. (2010). Advances in exercise, fitness, and
performance genomics. Med. Sci. Sports Exerc. 42, 835-846.
Ravussin, E., Lillioja, S., Anderson, T. E., Christin, L. and Bogardus, C. (1986).
Determinants of 24-hour energy expenditure in man. Methods and results using a
respiratory chamber. J. Clin. Invest. 78, 1568-1578.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
228T. Garland, Jr and others
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. and Dingemanse, N. J. (2007).
Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291-318.
Rezende, E. L., Gomes, F. R., Chappell, M. A. and Garland, T., Jr (2009). Running
behavior and its energy cost in mice selectively bred for high voluntary locomotor
activity. Physiol. Biochem. Zool. 82, 662-679.
Rhodes, J. S. and Garland, T., Jr (2003). Differential sensitivity to acute
administration of Ritalin, apomorphine, SCH 23390, and raclopride in mice
selectively bred for hyperactive wheel-running behavior. Psychopharmacology 167,
Rhodes, J. S. and Kawecki, T. J. (2009). Behavior and neurobiology. In Experimental
Evolution: Concepts, Methods, and Applications of Selection Experiments (ed. T.
Garland Jr and M. R. Rose), pp. 263-300. Berkeley, CA: University of California
Rhodes, J. S., Hosack, G. R., Girard, I., Kelley, A. E., Mitchell, G. S. and Garland,
T., Jr (2001). Differential sensitivity to acute administration of cocaine, GBR 12909,
and fluoxetine in mice selectively bred for hyperactive wheel-running behavior.
Psychopharmacol. 158, 120-131.
Rhodes, J. S., Garland, T., Jr and Gammie, S. C. (2003a). Patterns of brain activity
associated with variation in voluntary wheel-running behavior. Behav. Neurosci. 117,
Rhodes, J. S., Van Praag, H., Jeffrey, S., Girard, I., Mitchell, G. S., Garland, T., Jr
and Gage, F. H. (2003b). Exercise increases hippocampal neurogenesis to high
levels but does not improve spatial learning in mice bred for increased voluntary
wheel running. Behav. Neurosci. 117, 1006-1016.
Rhodes, J. S., Gammie, S. C. and Garland, T., Jr (2005). Neurobiology of mice
selected for high voluntary wheel-running activity. Integr. Comp. Biol. 45, 438-455.
Rhodes, R. E. and Smith, N. E. (2006). Personality correlates of physical activity: a
review and meta-analysis. Br. J. Sports Med. 40, 958-965.
Ricci, M. R. and Levin, B. E. (2003). Ontogeny of diet-induced obesity in selectively
bred Sprague-Dawley rats. Am. J. Physiol. Regul. Integr. Comp. Physiol. 285, R610-
Rodnick, K. J., Reaven, G. M., Haskell, W. L., Sims, C. R. and Mondon, C. E.
(1989). Variations in running activity and enzymatic adaptations in voluntary running
rats. J. Appl. Physiol. 66, 1250-1257.
Rowland, T. W. (1998). The biological basis of physical activity. Med. Sci. Sports
Exerc. 30, 392-399.
Sakurai, T., Amemiya, A., Ishii, M., Matsuzaki, I., Chemelli, R. M., Tanaka, H.,
Williams, S. C., Richardson, J. A., Kozlowski, G. P., Wilson, S. et al. (1998).
Orexins and orexin receptors: a family of hypothalamic neuropeptides and G protein-
coupled receptors that regulate feeding behavior. Cell 92, 573-585.
Salamone, J. D. (1992). Complex motor and sensorimotor functions of striatal and
accumbens dopamine: involvement in instrumental behavior processes.
Psychopharmacology (Berl.) 107, 160-174.
Salamone, J. D. and Correa, M. (2002). Motivational views of reinforcement:
implications for understanding the behavioral functions of nucleus accumbens
dopamine. Behav. Brain Res. 137, 3-25.
Sallis, J. F. and Glanz, K. (2006). The role of built environments in physical activity,
eating, and obesity in childhood. Future Child. 16, 89-108.
Saris, W. H., van Erp-Baart, M. A., Brouns, F., Westerterp, K. R. and ten Hoor, F.
(1989). Study on food intake and energy expenditure during extreme sustained
exercise: the Tour de France. Int. J. Sports Med. 10, S26-S31.
Scheurink, A. J. W., Boersma, G. J., Nergårdh, R. and Södersten, P. (2010).
Neurobiology of hyperactivity and reward: agreeable restlessness in Anorexia
Nervosa. Physiol. Behav. 100, 490-495.
Schoeller, D. A. (2009). The energy balance equation: looking back and looking
forward are two very different views. Nutr. Rev. 67, 249-254.
Schuld, A., Hebebrand, J., Geller, F. and Pollmacher, T. (2000). Increased body-
mass index in patients with narcolepsy. Lancet 355, 1274-1275.
Schultz, W. (2001). Reward signaling by dopamine neurons. Neuroscientist 7, 293-
Schultz, W. (2002). Getting formal with dopamine and reward. Neuron 36, 241-263.
Sclafani, A. (1984). Animal models of obesity: classification and characterization. Int.
J. Obes. 8, 491-508.
Seabra, A. F., Mendonc, D. M., Goring, H. H., Thomis, M. A. and Maia, J. A.
(2008). Genetic and environmental factors in familial clustering in physical activity.
Eur. J. Epidemiol. 23, 205-211.
Secor, S. M. (2009). Specific dynamic action: a review of the postprandial metabolic
response. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 179, 1-56.
Sevy, S., Hassoun, Y., Bechara, A., Yechiam, E., Napolitano, B., Burdick, K.,
Delman, H. and Malhotra, A. (2006). Emotion-based decision-making in healthy
subjects: short-term effects of reducing dopamine levels. Psychopharmacology (Berl.
) 188, 228-235.
Shepard, E. L. C., Wilson, R. P., Quintana, F., Laich, A. G., Liebsch, N., Albareda,
D. A., Halsey, L. G., Gleiss, A., Morgan, D. T., Myers, A. E. et al. (2008).
Identification of animal movement patterns using tri-axial accelerometry. Endanger.
Species Res. 10, 47-60.
Shephard, R. (2003). Limits to the measurement of habitual physical activity by
questionnaires. Br. J. Sports Med. 37, 197-206.
Sherwin, C. M. (1998). Voluntary wheel running: a review and novel interpretation.
Anim. Behav. 56, 11-27.
Sherwin, C. M. and Nicol, C. J. (1996). Reorganization of behaviour in laboratory
mice, Mus musculus, with varying cost of access to resources. Anim. Behav. 51,
Sih, A., Bell, A. and Johnson, J. C. (2004). Behavioral syndromes: an ecological and
evolutionary overview. Trends Ecol. Evol. 19, 372-378.
Simoes, P., Santos, J. and Matos, M. (2009). Experimental evolutionary
domestication. In Experimental Evolution: Concepts, Methods, and Applications of
Selection Experiments (ed. T. Garland Jr and M. R. Rose), pp. 89-110. Berkeley,
CA: University of California Press.
Simoncic, M., Horvat, S., Stevenson, P. L., Bunger, L., Holmes, M. C., Kenyon, C.
J., Speakman, J. R. and Morton, N. M. (2008). Divergent physical activity and
novel alternative responses to high fat feeding in polygenic fat and lean mice.
Behav. Genet. 38, 292-300.
Simonen, R. L., Rankinen, T., Perusse, L., Leon, A. S., Skinner, J. S., Wilmore, J.
H., Rao, D. C. and Bouchard, C. (2003a). A dopamine D2 receptor gene
polymorphism and physical activity in two family studies. Physiol. Behav. 78, 751-
Simonen, R., Rankinen, T., Perusse, L., Rice, T., Rao, D., Chagnon, Y. and
Bouchard, C. (2003b). Genome-wide linkage scan for physical activity levels in the
Quebec Family study. Med. Sci. Sports Exerc. 35, 1355-1359.
Solinas, M., Thiriet, N., El Rawas, R., Lardeux, V. and Jaber, M. (2009).
Environmental enrichment during early stages of life reduces the behavioral,
neurochemical, and molecular effects of cocaine. Neuropsychopharmacol. 34, 1102-
Sparling, P. B., Giuffrida, A., Piomelli, D., Rosskopf, L. and Dietrich, A. (2003).
Exercise activates the endocannabinoid system. Neuroreport 14, 2209-2211.
Speakman, J. R. (1997). Doubly Labeled Water: Theory and Practice, p. 399. London
and New York: Chapman and Hall.
Speakman, J. R. (2010). FTO effect on energy demand versus food intake. Nature.
Speakman, J. R. and Selman, C. (2003). Physical activity and resting metabolic rate.
Proc. Nutr. Soc. 62, 621-634.
Speakman, J. R., Rance, K. A. and Johnstone, A. M. (2008). Polymorphisms of the
FTO gene are associated with variation in energy intake, but not energy expenditure.
Obesity 16, 1961-1965.
Stamps, J. and Groothuis, T. G. G. (2010). The development of animal personality:
relevance, concepts and perspectives. Biol. Rev. 85, 301-325.
Steimer, T. and Driscoll, P. (2003). Divergent stress responses and coping styles in
psychogenetically selected Roman high-(RHA) and low-(RLA) avoidance rats:
behavioural, neuroendocrine and developmental aspects. Stress 6, 87-100.
Steimer, T., Python, A., Driscoll, P. and de Saint, H. Z. (1999). Psychogenetically
selected (Roman high- and low-avoidance) rats differ in 24-hour sleep organization.
J. Biol. Rhythms 14, 221-226.
Stubbe, J. H. and de Geus, E. J. C. (2009). Genetics of exercise behavior. In
Handbook of Behavior Genetics (ed. Y.-K. Kim), pp. 343-358. New York: Springer
Science and Business Media.
Sutin, A. R., Costa, P. T., Jr, Uda, M., Ferrucci, L., Schlessinger, D. and
Terracciano, A. (2010). Personality and metabolic syndrome. Age 32, 513-519.
Svenson, K. L., Smith, R. V., Magnani, P. A., Suetin, H. R., Paigen, B., Naggert, J.
K., Li, R., Churchill, G. A. and Peters, L. L. (2007). Multiple trait measurements in
43 inbred mouse strains capture the phenotypic diversity characteristic of human
populations. J. Appl. Physiol. 102, 2369-2378.
Swallow, J. G., Carter, P. A. and Garland, T., Jr (1998). Artificial selection for
increased wheel-running behavior in house mice. Behav. Genet. 28, 227-237.
Swallow, J. G., Koteja, P., Carter, P. A. and Garland, T., Jr (1999). Artificial
selection for increased wheel-running activity in house mice results in decreased
body mass at maturity. J. Exp. Biol. 202, 2513-2520.
Swallow, J. G., Koteja, P., Carter, P. A. and Garland, T., Jr (2001). Food
consumption and body composition in mice selected for high wheel-running activity.
J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 171, 651-659.
Swallow, J. G., Rhodes, J. S. and Garland, T., Jr (2005). Phenotypic and
evolutionary plasticity of organ masses in response to voluntary exercise in house
mice. Integr. Comp. Biol. 45, 426-437.
Swallow, J. G., Hayes, J. P., Koteja, P. and Garland, T., Jr (2009). Selection
experiments and experimental evolution of performance and physiology. In
Experimental Evolution: Concepts, Methods, and Applications of Selection
Experiments (ed. T. Garland Jr and M. R. Rose), pp. 301-351. Berkeley, CA:
University of California Press.
Teran-Garcia, M., Rankinen, T. and Bouchard, C. (2008). Genes, exercise, growth,
and the sedentary, obese child. J. Appl. Physiol. 105, 988-1001.
Teske, J. A., Levine, A. S., Kuskowski, M., Levine, J. A. and Kotz, C. M. (2006).
Elevated hypothalamic orexin signaling, sensitivity to orexin A, and spontaneous
physical activity in obesity-resistant rats. Am. J. Physiol. Regul. Integr. Comp.
Physiol. 291, R889-R899.
Teske, J. A., Billington, C. J. and Kotz, C. M. (2008). Neuropeptidergic mediators of
spontaneous physical activity and non-exercise activity thermogenesis.
Neuroendocrinology 87, 71-90.
Thorburn, A. W. and Proietto, J. (2000). Biological determinants of spontaneous
physical activity. Obes. Rev. 1, 87-94.
Thorpe, A. J., Mullett, M. A., Wang, C. and Kotz, C. M. (2003). Peptides that
regulate food intake: regional, metabolic, and circadian specificity of lateral
hypothalamic orexin A feeding stimulation. Am. J. Physiol. Regul. Integr. Comp.
Physiol. 284, R1409-R1417.
Timberlake, W. and Wozny, M. (1979). Reversibility of reinforcement between eating
and running by schedule changes – comparison of hypotheses and models. Anim.
Learn. Behav. 7, 461-469.
Timmons, J. A., Knudsen, S., Rankinen, T., Koch, L. G., Sarzynski, M., Jensen, T.,
Keller, P., Scheele, C., Vollaard, N. B. J., Nielsen, S. et al. (2010). Using
molecular classification to predict gains in maximal aerobic capacity following
endurance exercise training in humans. J. Appl. Physiol. 108, 1487-1496.
Tokuyama, K., Saito, M. and Okuda, H. (1982). Effects of wheel running on food
intake and weight gain of male and female rats. Physiol. Behav. 28, 899-903.
Tomporowski, P. D., Davis, C. L., Miller, P. H. and Naglieri, J. A. (2008). Exercise
and children’s intelligence, cognition, and academic achievement. Educ. Psychol.
Rev. 20, 111-131.
Tou, J. C. L. and Wade, C. E. (2002). Determinants affecting physical activity levels in
animal models. Exp. Biol. Med. Vol. 227, 587-600.
Tremblay, A., Pérusse, L. and Bouchard, C. (2004). Energy balance and body-
weight stability: impact of gene-environment interactions. Br. J. Nutr. 92, S63-S66.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
229Exercise, spontaneous activity and obesity Download full-text
Trivedi, P., Yu, H., MacNeil, D. J., van der Ploeg, L. H. T. and Guan, X. M. (1998).
Distribution of orexin receptor mRNA in the rat brain. FEBS Lett. 438, 71-75.
Turkheimer, E. (2000). Three laws of behavior genetics and what they mean. Curr.
Dir. Psychol. Sci. 9, 160-164.
Vaanholt, L. M., Garland, T., Jr, Daan, S. and Visser, G. H. (2007a). Wheel-running
activity and energy metabolism in relation to ambient temperature in mice selected
for high wheel-running activity. J. Comp. Physiol. B 177, 109-118.
Vaanholt, L. M., De Jong, B., Garland, T., Jr, Daan, S. and Visser, G. H. (2007b).
Behavioural and physiological responses to increased foraging effort in male mice. J.
Exp. Biol. 210, 2013-2024.
Vaanholt, L. M., Meerlo, P., Garland, T., Jr, Visser, G. H. and van Dijk, G. (2007c).
Plasma adiponectin is increased in mice selectively bred for high wheel-running
activity, but not by wheel running per sé. Horm. Metab. Res. 39, 377-383.
Vaanholt, L. M., Jonas, I., Doornbos, M., Schubert, K. A., Nyakas, C., Garland, T.,
Jr, Visser, G. H. and van Dijk, G. (2008). Metabolic and behavioral responses to
high-fat feeding in mice selectively bred for high wheel-running activity. Int. J.
Obesity. 32, 1566-1575.
van der Kooij, M. A. and Glennon, J. C. (2007). Animal models concerning the role
of dopamine in attention-deficit hyperactivity disorder. Neurosci. Biobehav. Rev. 31,
van Oers, K., de Jong, G., Drent, P. J. and van Noordwijk, A. J. (2004). A genetic
analysis of avian personality traits: correlated, response to artificial selection. Behav.
Genet. 34, 611-619.
van Overveld, T. and Matthysen, E. (2010). Personality predicts spatial responses to
food manipulations in free-ranging great tits (Parus major). Biol. Lett. 6, 187-190.
van Praag, H. (2009). Exercise and the brain: something to chew on. Trends Neurosci.
van Praag, H., Christie, B. R., Sejnowski, T. J. and Gage, F. H. (1999). Running
enhances neurogenesis, learning, and long-term potentiation in mice. Proc. Natl.
Acad. Sci. USA 96, 13427-13431.
Vehrencamp, S. L., Bradbury, J. W. and Gibson, R. M. (1989). The energetic cost of
display in male sage grouse. Anim. Behav. 38, 885-896.
Verbeek, P., Iwamoto, T. and Murakami, N. (2008). Variable stress-responsiveness
in wild type and domesticated fighting fish. Physiol. Behav. 93, 83-88.
Viggiano, D. (2008). The hyperactive syndrome: Metanalysis of genetic alterations,
pharmacological treatments and brain lesions which increase locomotor activity.
Behav. Brain Res. 194, 1-14.
Volkow, N. D., Fowler, J. S., Wang, G. J. and Swanson, J. M. (2004). Dopamine in
drug abuse and addiction: results from imaging studies and treatment implications.
Mol. Psychiatr. 9, 557-569.
Walker, C. and Byers, J. A. (1991). Heritability of locomotor play in house mice, Mus
domesticus. Anim. Behav. 42, 891-898.
Waters, R. P., Renner, K. J., Pringle, R. B., Summers, C. H., Britton, S. L., Koch, L.
G. and Swallow, J. G. (2008). Selection for aerobic capacity affects corticosterone,
monoamines and wheel-running activity. Physiol. Behav. 93, 1044-1054.
Ways, J., Smith, B., Barbato, J., Ramdath, R., Pettee, K., DeRaedt, S., Allison, D.,
Koch, L., Lee, S. and Cicila, G. (2007). Congenic strains confirm aerobic running
capacity quantitative trait loci on rat chromosome 16 and identify possible
intermediate phenotypes. Physiol. Genomics 29, 91-97.
Weathers, W. W., Buttemer, W. A., Hayworth, A. M. and Nagy, K. A. (1984). An
evaluation of time-budget estimates of daily energy expenditure in birds. Auk 101,
Welk, G. J., McClain, J. J., Eisenmann, J. C. and Wickel, E. E. (2007). Field
validation of the MTI Actigraph and BodyMedia armband monitor using the IDEEA
monitor. Obesity 15, 918-928.
Werme, M., Thoren, P., Olson, L. and Brene, S. (2000). Running and cocaine both
upregulate dynorphin mRNA in medial caudate putamen. Eur. J. Neurosci. 12, 2967-
Westerterp, K. R. (2008). Physical activity as determinant of daily energy expenditure.
Physiol. Behav. 93, 1039-1043.
Westerterp, K. R. (2009). Assessment of physical activity: a critical appraisal. Eur. J.
Appl. Physiol. 105, 823-828.
Westerterp, K. R. and Plasqui, G. (2004). Physical activity and human energy
expenditure. Curr. Opin. Clin. Nutr. Metab. Care. 7, 607-613.
Westerterp, K. R. and Speakman, J. R. (2008). Physical activity energy expenditure
has not declined since the 1980s and matches energy expenditures of wild
mammals. Int. J. Obesity 32, 1256-1263.
Wickel, E. E. and Eisenmann, J. C. (2006). Within- and between-individual variability
in estimated energy expenditure and habitual physical activity among young adults.
Eur. J. Clin. Nutr. 60, 538-544.
Widdowson, E. M., Edholm, O. G. and McCance, R. A. (1954). The food intake and
energy expenditure of cadets in training. Br. J. Nutr. 8, 147-155.
Wise, R. A. (2004). Dopamine, learning and motivation. Nat. Rev. Neurosci. 5, 483-
Wolf, M., van Doorn, G. S., Leimar, O. and Weissing, F. J. (2007). Life-history trade-
offs favour the evolution of animal personalities. Nature 447, 581-584.
Wolf, M., van Doorn, G. S. and Weissing, F. J. (2008). Evolutionary emergence of
responsive and unresponsive personalities. Proc. Natl. Acad. Sci. USA 105, 15825-
Wone, B., Sears, M. W., Labocha, M. K., Donovan, E. R. and Hayes, J. P. (2009).
Genetic variances and covariances of aerobic metabolic rates in laboratory mice.
Proc. R. Soc. Lond. B Biol. Sci. 276, 3695-3704.
Yamamoto, Y., Tanahashi, T., Kawai, T., Chikahisa, S., Katsuura, S., Nishida, K.,
Teshima-Kondo, S., Sei, H. and Rokutan, K. (2009). Changes in behavior and
gene expression induced by caloric restriction in C57BL/6 mice. Physiol. Genomics
Young, R. J. (2003). Environmental Enrichment for Captive Animals (UFAW Animal
Welfare), p. 228. Oxford, UK: Blackwell Science, Limited.
Zheng, H., Lenard, N. R., Shin, A. C. and Berthoud, H. R. (2009). Appetite control
and energy balance regulation in the modern world: reward-driven brain overrides
repletion signals. Int. J. Obes. 33, S8-S13.
Zombeck, J. A., DeYoung, E., Brzezinska, W. J. and Rhodes, J. S. (2011).
Selective breeding for increased home cage physical activity in Collaborative Cross
and Hsd:ICR mice. Behav. Genet. In press.
Zurlo, F., Ferraro, R. T., Fontvielle, A. M., Rising, R., Bogardus, C. and Ravussin,
E. (1992). Spontaneous physical activity and obesity: cross-sectional and
longitudinal studies in Pima Indians. Am. J. Physiol. Endocrinol. Metab. 263, E296-
THE JOURNAL OF EXPERIMENTAL BIOLOGY