Low levels of physical activity are associated with dysregulation of energy intake and fat mass gain over 1 year^,

Abstract

Background: Previous studies suggest that appetite may be dysregulated at low levels of activity, creating an energy imbalance that results in weight gain.

Objective: The aim was to examine the relation between energy intake, physical activity, appetite, and weight gain during a 1-y follow-up period in a large sample of adults.

Design: Participants included 421 individuals (mean ± SD age: 27.6 ± 3.8 y). Measurements included the following: energy intake with the use of interviewer-administered dietary recalls and calculated by using changes in body composition and energy expenditure, moderate-to-vigorous physical activity (MVPA) with the use of an arm-based monitor, body composition with the use of dual-energy X-ray absorptiometry, and questionnaire-derived perceptions of dietary restraint, disinhibition, hunger, and control of eating. Participants were grouped at baseline into quintiles of MVPA (min/d) by sex. Measurements were repeated every 3 mo for 1 y.

Results: At baseline, an inverse relation existed between body weight and activity groups, with the least-active group (15.7 ± 9.9 min MVPA/d, 6062 ± 1778 steps/d) having the highest body weight (86.3 ± 13.2 kg) and the most-active group (174.5 ± 60.5 min MVPA/d, 10260 ± 3087 steps/d) having the lowest body weight (67.5 ± 11.0 kg). A positive relation was observed between calculated energy intake and activity group, except in the lowest quintile of activity. The lowest physical activity group reported higher levels of disinhibition (P = 0.07) and cravings for savory foods (P = 0.03) compared with the group with the highest level of physical activity. Over 1 y of follow-up, the lowest activity group gained the largest amount of fat mass (1.7 ± 0.3 kg) after adjustment for change in MVPA and baseline fat mass. The odds of gaining >3% of fat mass were between 1.8 and 3.8 times as high for individuals in the least-active group as for those in the middle activity group.

Conclusions: These results suggest that low levels of physical activity are a risk factor for fat mass gain. In the current sample, a threshold for achieving energy balance occurred at an activity level corresponding to 7116 steps/d, an amount achievable by most adults. This trial was registered at clinicaltrials.gov as NCT01746186.

INTRODUCTION

Nearly 60 y ago, Jean Mayer published the results of studies involving mice (1), rats (2), and humans (3) that showed a mismatch between energy intake and energy expenditure at low levels of physical activity. Among humans, the energy intakes of sedentary shopkeepers, supervisors, and clerks at the Ludlow Jute Company in West Bengal (Calcutta), India, were nearly as high as the most active employees who were blacksmiths, coalmen, and bale carriers. The body weights of these sedentary employees were the highest of all of the employees, whereas those performing more physically demanding tasks (i.e., in occupations requiring light, medium, heavy, and very heavy work) had lower body weights that were similar between groups. Because of the homogeny of these weights, Mayer classified this level of work to be within a “normal activity range” for the optimal regulation of body weight (3). It was hypothesized that energy intake became dysregulated at low levels of physical activity, resulting in a chronic mismatch with energy expenditure that ultimately results in obesity.

A limited number of studies have attempted to explore this hypothesis further. For example, findings from a small sample of men (n = 6) confined to a whole-room calorimeter suggested that short-term sedentary behavior (<7 d) is associated with an acute positive energy balance due to uncoupling of energy expenditure and energy intake (4). When an individual becomes more physically active, appetite sensitivity improves, as shown by a moderate-intensity exercise intervention by Martins et al. (5). In addition, individuals who are already active were shown to regulate appetite better (measured by satiety quotient, representing appetite rating after a test meal, and after adjustment for the amount of food consumed) and to have a lower energy intake during a single meal (6). This finding suggests a “normal activity range” for the regulation of appetite similar to Mayer’s original hypothesis. Finally, a 12-wk exercise intervention in overweight/obese participants resulted in increased satiety in response to a fixed meal, increasing the sensitivity of the physiologic system to suppress hunger after a meal (7, 8)

The purpose of the present study was to examine the relation between energy intake, physical activity, and body weight in a large sample (n = 421) of young adults, which represents an opportunity to expand the original findings of Mayer et al. (3) by objectively measuring physical activity as opposed to relying on estimated occupational activity. A secondary aim was to identify associations between levels of physical activity and subjective appetite regulation to understand the relation with energy intake. Finally, we explored the long-term implications of physical activity level on changes in body composition over 12 mo with the goal of identifying a threshold for the prevention of weight gain.

METHODS

Participants and enrollment process

The methodology of the current study has been described in detail previously (9). Briefly, participants were young adults, aged ≥21 to ≤35 y, with a BMI (in kg/m²) ≥20 to ≤35. Individuals were ineligible for the study for reasons that might influence body weight status (use of medications to lose weight, initiation or cessation of smoking in the previous 6 mo, or planned weight-loss surgery). Individuals also were excluded for elevated blood pressure (resting blood pressure >150 mm Hg systolic and/or >90 mm Hg diastolic), abnormal metabolic health (ambulatory blood glucose >145 mg/dL), or current diagnosis of or taking medications for a major chronic health condition. In addition, individuals with a history of depression, anxiety, or panic were excluded, as were those taking selective serotonin inhibitors for any reason. All women were eumenorrheic, and those who gave birth in the previous 12 mo or were planning to begin or stop birth control during the study also were excluded. All study protocols were approved by the University of South Carolina Institutional Review Board, and informed consent was obtained from each participant before data collection.

Anthropometric and body-composition measurements

Fat mass (FM)¹² and fat-free mass (FFM) were measured by using a whole-body dual-energy X-ray absorptiometer (Lunar DPX system, version 3.6; Lunar Radiation Corporation). All anthropometric measurements were performed with the participant dressed in surgical scrubs and in bare feet. BMI [weight (kg)/height (m)²] was calculated from the average of 3 height and weight measurements by using a wall-mounted stadiometer and electronic scale and recorded to the nearest 0.1 cm and 0.1 kg, respectively. All anthropometric and body-composition measurements were completed once every 3 mo for the duration of the study.

Energy expenditure

Energy expenditure was measured by using an arm-based physical activity monitor (SenseWear Mini Armband; BodyMedia Inc.), which incorporates triaxial accelerometry and measures of heat flux, galvanic skin response, skin temperature, and near-body ambient temperature to estimate total daily energy expenditure and time spent in moderate-to-vigorous physical activity [MVPA; defined as at least 8 of 10 consecutive minutes of activity at ≥3.0 metabolic task equivalents (METs)] (10). The armband has been shown to be a valid device for measuring energy expenditure and physical activity (11–13). The participants were asked to wear the armband for 10 d and were deemed compliant if they completed 7 d of wear (including 2 weekend days) with ≥18.5 h of wear time on each of the days. If the armband was removed from the body, participants recorded the activities performed in a log, and these nonwear activities were added to the energy expenditure values on the basis of the corresponding MET values according to the 2011 Compendium of Physical Activities (14). Energy expenditure for these nonwear periods was calculated as the MET value (MET-min) times the individual’s laboratory-measured resting metabolic rate (RMR). In addition, the armband provided an estimate of steps taken per day (15, 16). Measurements of total daily energy expenditure and time spent in MVPA were completed once every 3 mo for the duration of the study and corresponded to the measurement of energy intake that began immediately after the assessment of body-composition and metabolic measurements. Participants were grouped by level of physical activity on the basis of quintiles of MVPA (min/d) among the entire cohort, by sex. RMR was measured at baseline via indirect calorimetry by using a ventilated hood and an open-circuit system (TrueOne 2400 Metabolic Measurement Cart; ParvoMedics).

Energy intake

Energy intake (hereafter “reported energy intake”) was measured by using interviewer-administered 24-h dietary recalls utilizing the Nutrient Data System for Research software (version 2012; University of Minnesota Nutrition Coordinating Center) (17). Three dietary recalls occurred on randomly selected nonconsecutive days over a 2-wk period (including at least 1 weekend day) to minimize preparation that could bias recall by the participants (18) and were repeated every 3 mo. The dietary recalls were collected by a team of experienced (>6 y using the Nutrient Data System for Research) registered dietitians who used a multipass approach, which utilizes prompting to reduce food omissions and standardizes the interview methodology across interviewers (19). Before data collection, study participants underwent a brief training session (10–15 min) on how to estimate portion sizes of commonly eaten foods. The training incorporated life-sized plates, glasses, and utensils and food models in a hands-on experiential interchange (20). Reported energy intake was divided by measured RMR to identify plausible data, with a ratio of <1.35 indicating probable underreporting (21, 22).

Energy intake (hereafter “calculated energy intake”) also was estimated from baseline to month 3 on the basis of a validated equation based on the concept of energy balance. Energy balance is based on the First Law of Thermodynamics (the Law of Conservation of Energy), which can be represented by using the following equation:

where “energy storage” represents rate of change in energy stores (FFM and FM), “energy intake” represents the rate of energy intake, and “energy expenditure” represents the rate of energy expenditure. When 2 terms of the energy balance equation are known, it is possible to solve for the third term. Thus, calculated energy intake was estimated on the basis of the following validated equation (23–25):

where ΔFFM and ΔFM represent change in each variable as measured by dual-energy X-ray absorptiometry between baseline and month 3, Δt represents days between baseline and month 3, 1020 represents the energy density in kilocalories of FFM per kilogram, and 9500 represents the energy density of FM per kilogram, both based on established values (26); EE represents energy expenditure as measured by the activity monitor average at baseline and month 3.

Questionnaires

Perceptions of dietary restraint, disinhibition, and hunger were measured by using the Three-Factor Eating Questionnaire (27). Cognitive restraint refers to the cognitive control to restrict food intake to achieve better control of weight (e.g., avoiding fattening foods or eating smaller portions are strategies to limit energy intake) (28, 29). Disinhibition assesses eating in response to emotional, cognitive, or social cues (30), such as overeating and eating opportunistically in an obesogenic environment (29). Hunger measures the extent to which hunger feelings are perceived and the extent to which such feelings evoke food intake [e.g., feeling so hungry that a person eats >3 times/d (29)]. The Control of Eating Questionnaire was used to assess hunger, fullness, cravings, the desire to eat certain types of foods, and the ability to resist urges to eat (31, 32).

Statistical analyses

Participant characteristics were based on demographic and physiologic measurements and portrayed by using means and SDs for continuous variables and percentages for categorical variables. Significance for comparison between groups was tested by using ANOVA for continuous variables and chi-square tests for categorical variables. Logistic regression was used to calculate ORs and 95% CIs for clinically significant weight gain (33) across activity groups. A linear mixed modeling (LMM) regression random-intercept growth model was used to analyze the longitudinal data (34) for changes in 1) total body weight followed by 2) FM. The LMM approach was chosen because it allows for unbalanced observations over time, making it ideal to analyze longitudinal data. The LMM was repeated multiple times by using various covariance structures: unstructured, first-order autoregressive, and compound symmetry. The unstructured covariance structure did not converge, and the results presented here used the first-order autoregressive structure due to a lower Akaike information criterion value compared with compound symmetry covariance structure. All computations were performed with the use of SAS version 9.3 (SAS Institute).

RESULTS

Participants were grouped by physical activity level on the basis of daily MVPA, with group 1 representing the lowest quintile and group 5 representing the highest quintile. Baseline characteristics for each group are presented in Table 1. The most-active group accumulated 174.5 ± 60.5 min of MVPA/d, whereas the least-active group performed 15.7 ± 9.9 min/d. When expressed as steps per day, physical activity was lowest in group 1 (6062 ± 1778 steps/d) and increased incrementally across each subsequent group (P-trend < 0.0001), with group 5 being the most active (10,260 ± 3087 steps/d). Body weight and BMI were both significantly different across groups, with the lowest physical activity group having the highest body weight and BMI (86.3 ± 13.2 kg and 29.6 ± 3.3, respectively) and the highest physical activity group with the lowest body weight and BMI (67.5 ± 11.0 kg and 23.0 ± 2.3, respectively). The difference in body weight was due entirely to differences in FM (group 1 compared with group 5: 30.9 ± 9.0 compared with 14.2 ± 5.6 kg; P < 0.0001), because FFM did not vary between groups (group 1 compared with group 5: 55.4 ± 12.3 compared with 53.3 ± 12.0 kg; P = 0.51).

TABLE 1.

Baseline characteristics of participants by physical activity group¹

	Physical activity group
	1 (n = 85)	2 (n = 84)	3 (n = 84)	4 (n = 84)	5 (n = 84)	P (between-group differences)
MVPA,2 min/d	15.7 ± 9.9	39.2 ± 16.2	63.3 ± 22.4	95.4 ± 27.8	174.5 ± 60.5	<0.0001
Age, y	28.8 ± 3.4	28.3 ± 3.9	27.4 ± 3.8	27.5 ± 3.7	26.2 ± 3.7	<0.0001
Female, %	51.8	51.2	51.2	51.2	51.2	0.99
White, %	54.1	60.7	65.5	72.6	81.0	0.0002
BMI, kg/m2	29.6 ± 3.3	26.8 ± 3.6	25.2 ± 3.0	23.5 ± 2.5	23.0 ± 2.3	<0.0001
Body weight, kg	86.3 ± 13.2	79.5 ± 12.5	73.2 ± 12.7	69.3 ± 10.6	67.5 ± 11.0	<0.0001
Fat mass, kg	30.9 ± 9.0	24.6 ± 10.0	20.3 ± 8.3	16.4 ± 6.7	14.2 ± 5.6	<0.0001
Fat-free mass, kg	55.4 ± 12.3	55.0 ± 12.0	52.9 ± 12.7	52.9 ± 11.6	53.3 ± 12.0	0.51
RMR, kcal/d	1579.2 ± 253.3	1563.8 ± 254.2	1504.9 ± 259.4	1490.6 ± 272.3	1492.9 ± 264.1	0.07
Steps, number/d	6062 ± 1778	6565 ± 2087	7116 ± 2000	8266 ± 2329	10260 ± 3087	<0.0001
Energy expenditure,3 kcal/d	2668 ± 431	2679 ± 449	2652 ± 492	2721 ± 483	2989 ± 616	<0.0001
Activity energy expenditure,4 kcal/d	96 ± 67	218 ± 106	322 ± 145	470 ± 180	852 ± 360	<0.0001

¹Values are means ± SDs. MVPA, moderate-to-vigorous physical activity (time spent in); RMR, resting metabolic rate.

²Mean wear time of the activity monitor (23.2 ± 0.8 h/d) did not differ between groups (P = 0.46).

³Estimated by using Sensewear Mini monitor (BodyMedia, Inc.).

⁴Energy expended during activity of ≥3.0 metabolic equivalent tasks.

Reported energy intake was not significantly different (P = 0.40) between activity groups (Table 2), with the highest intakes occurring in the most-active group (2190 ± 799 kcal/d) and the lowest intakes in the middle-activity group (2004 ± 590 kcal/d). However, the ratio between reported energy intake and RMR for groups 1, 2, and 3 was <1.35, indicating potential underreporting (21); interpretation of these values should therefore be made with caution. Calculated energy intake, as estimated by using change in body composition and energy expenditure, was higher than reported energy intake by >600 kcal/d for each group. Body composition changed little, so calculated energy intake was primarily determined by energy expenditure (Supplemental Table 1).

TABLE 2.

Baseline energy intake as reported by participants and calculated on the basis of changes in body composition and energy expenditure¹

	Physical activity group
	1 (n = 85)	2 (n = 84)	3 (n = 84)	4 (n = 84)	5 (n = 84)	P (between-group differences)
Reported energy intake,2 kcal/d	2033 ± 656	2059 ± 656	2004 ± 590	2124 ± 669	2190 ± 799	0.40
Reported energy intake:resting metabolic rate3	1.30 ± 0.38	1.33 ± 0.39	1.34 ± 0.37	1.43 ± 0.38	1.47 ± 0.45	0.03
Calculated energy intake,4 kcal/d	2691 ± 477	2646 ± 467	2674 ± 517	2698 ± 501	2866 ± 610	0.07

¹Values are means ± SDs.

²The mean number of dietary recalls completed (2.8 ± 0.5/time point) did not differ between groups (P = 0.65).

³Values <1.35 indicate implausible energy intake values.

⁴Estimated from changes in energy balance between baseline and month 3.

Figure 1 shows the relation between physical activity group and calculated energy intake and/or body weight and is presented in the current format to allow comparisons with Mayer et al.’s original 1956 findings (3). A negative linear relation was found between physical activity and body weight, with the least-active group weighing the most and the most-active group weighing the least (P < 0.01; Figure 1A). Calculated energy intake did not differ by physical activity group, although a trend of increasing energy intake with increasing physical activity was observed, with the exception of the least active group (P = 0.07 for main group effect; Figure 1B). Figure 2 shows the results of the Three-Factor Eating Questionnaire after adjustment for body weight, with levels of disinhibition tending to be higher (P = 0.07) in the lowest physical activity group compared with the middle physical activity group. Unadjusted levels of disinhibition were higher in the low activity group than in all of the other groups (P < 0.05). Individuals in the lowest physical activity group also had significantly higher cravings for savory foods (French fries, burgers, pizza, etc.) compared with individuals in the highest physical activity group (5.6 ± 0.2 compared with 4.4 ± 0.3, P = 0.03; results not shown); no other differences were observed for any of the other items on the Control of Eating Questionnaire.

FIGURE 1.

Relation between physical activity group and body weight (A) and calculated energy intake (B). Group 1, n = 83; group 2, n = 84; group 3, n = 84; group 4, n = 84; and group 5, n = 84.

FIGURE 2.

TFEQ scores for cognitive restraint (A), disinhibition (B), and hunger (C) adjusted for body weight across levels of physical activity. Error bars represent SEs. Tests for group differences were completed by using ANOVA. Group 1, n = 85; group 2, n = 84; group 3, n = 84; group 4, n = 84; and group 5, n = 84. ⁺P = 0.07. TFEQ, Three-Factor Eating Questionnaire.

Of the 421 participants enrolled at baseline, 344 were available for analysis at month 12 (group 1, n = 69; group 2, n = 70; group 3, n = 65; group 4, n = 71; and group 5, n = 69). Unadjusted changes in body weight, FM, and FFM between baseline and month 12 for all groups are presented in Supplemental Table 2. All groups except for group 3 (P = 0.30) showed significant gains in FM at 1 y (Figure 3); however, the least-active group, group 1, experienced the largest gain in FM (1.7 ± 0.3 kg), which was significantly higher than in all other groups (P < 0.05) after adjustment for change in MVPA and baseline FM. The relative increase in FM from baseline was highest in the least-active group (5.6%) followed by the most-active group (4.6%); however, this relative increase is somewhat misleading given the low baseline FM of the most active group. To identify a threshold of physical activity to prevent significant gains in FM over 12 mo of observation, logistic regression was performed by using 2 statistical conditions: model 1, with adjustments made for baseline FM, and model 2, with adjustments made for baseline FM plus changes in MVPA from baseline to month 12. Individuals in the lowest and second lowest physical activity groups were 2.57 (P < 0.01) and 1.82 (P = 0.07) more likely, respectively, to increase their FM by 3% compared with the middle physical activity group in model 1; this OR increased to 3.80 (P < 0.01) and 2.21 (P = 0.03) in model 2 (Figure 4). Individuals in the 2 most active groups were not at increased risk of gaining >3% of FM under either of the models.

FIGURE 3.

Change (Δ) in fat mass from baseline to 12 mo by physical activity group, adjusted for change in moderate-to-vigorous physical activity and baseline fat mass by linear mixed modeling. Error bars represent SEs. Group 1, n = 69; group 2, n = 70; group 3, n = 65; group 4, n = 71; and group 5, n = 69. *P < 0.05 for difference in change in fat mass between physical activity group 1 and all of the other groups.

FIGURE 4.

ORs (95% CIs) of increasing fat mass by >3% by physical activity group with the use of logistic regression; group 3 is the reference. Model 1 includes adjustments for baseline fat mass. Model 2 includes additional adjustment for change in moderate-to-vigorous physical activity (min/d) between baseline and month 12. Group 1, n = 69; group 2, n = 70; group 3, n = 65; group 4, n = 71; and group 5, n = 69.

DISCUSSION

There are 3 primary findings in the present study. First, we observed a positive relation between calculated energy intake and physical activity, with the exception of low levels of physical activity. Those performing the lowest amount of activity consumed more calories than did the next 2 higher activity groups and nearly as much as the third highest activity group. Second, individuals with low levels of physical activity had lower reported energy intakes but higher calculated energy intakes, higher levels of underreporting of energy intake, and higher levels of disinhibition than their more active peers. Third, individuals with low levels of physical activity experienced the largest gains in FM of all individuals, resulting in a 1.8–3.8 higher risk of gaining clinically significant amounts of FM over a 12-mo period. Taken together, our results support the hypothesis proposed by Mayer et al. (3) that a low level of physical activity is a risk factor for weight gain via an inability to achieve energy balance, which may ultimately lead to gains in FM. The threshold of physical activity at which optimal energy balance is achieved, thus preventing clinically significant FM gain, occurs at ∼7100 steps/d, a level of activity that is achievable by most adults.

The concept of dysregulation of appetite at low levels of physical activity is not new, with Jean Mayer exploring the topic in a series of studies in the 1950s. It was observed in mice (1), rats (2), and humans (3) that energy intake only increased proportionally with energy expenditure within a certain range of physical activity, which Mayer described as the “normal activity range,” later described by Blundell et al. (35) as the “zone of regulation.” Below this level, the relation between intake and expenditure became uncoupled, resulting in energy imbalance. The relation between physical activity groups and body weights and energy intake in the present study (Figure 1) are remarkably similar to those observed by Mayer et al. among factory workers in West Bengal, India, nearly 60 y ago (3). That investigation found a J-shaped relation between energy intake and activity based on job characteristics, with high energy intakes in the most-active and least-active individuals and the lowest energy intake in those doing “light work.” In our sample, physical activity levels >7116 steps/d corresponded to the threshold of the “normal activity range” or “zone of regulation.”

The rationale for the term “zone of regulation” is to denote the role of physical activity in the modulation of satiety signaling. The theory of the zone of regulation is important because it relates the accumulation of adipose tissue as not only occurring as a result of low amounts of energy expended but also that physical activity plays a regulator role in the amount of energy consumed via appetite signals. Higher levels of disinhibition (a tendency toward overeating and eating opportunistically in an obesogenic environment) (29) observed in the lowest physical activity group in the current study support the theory of dysregulation of appetite with low levels of physical activity. Also, the highest levels of underreporting of energy intake occurred in the 3 least-active groups of participants (Table 2). There is a critical need to determine the cause of this misreporting; it has been suggested that cognitive-emotional disconnect results in measurement error (36), a potential additional indicator of dysregulation of energy balance at low levels of activity. These findings are consistent with those of Hebert and colleagues, which show that response sets, which reflect similar underlying psychological attributes, are a major contributor to reporting errors encountered in assessing both diet (37, 38) and physical activity (39).

Studies with small sample sizes have shown results that are similar to ours. Six men spent 14 d in a whole-room chamber, 7 d of which were spent while sedentary and 7 d while being moderately active (4). During the sedentary phase, ad libitum energy intake remained elevated despite the restriction of energy expenditure by 740 kcal/d compared with the active condition. In addition, motivation to eat (preoccupation with thoughts of food and urge to eat) was higher during the sedentary condition; however, these differences were not significant, likely due to the small number of participants. Similarly, 8 men restricted their energy expenditure by 22–25% for 2 d, yet their energy intake remained stable (40). The failure to compensate for decreases in activity by concomitantly reducing energy intake supports the concept of a sedentary “nonregulated” zone in which a positive energy balance occurs resulting in the accumulation of FM (41).

Individuals in the lowest quintile of physical activity gained the largest amount of FM during 12 mo of observation (Figure 3), and those in the lowest 2 quintiles were between 1.8 and 3.8 times as likely to gain FM than was the middle quintile, whereas those in the middle quintile were the only participants not to gain FM. This suggests that the threshold level of physical activity to prevent weight gain occurs at a moderate volume, corresponding to favorable regulation of energy intake (Figure 1) and appetite (Figure 2). Harrington et al. (6) observed that men in the middle tertile of activity (184–243 kcal/d) had higher satiety quotients and lower energy intakes than did individuals who were more or less active. Likewise, Mayer noted that humans who performed “medium” levels of work closely matched their energy intake to expenditure (3) and rats restricted in their activity or exercising at high levels could not (2). Blundell (41) coined this level of energy expenditure as “optimum,” indicating an appropriate regulation of appetite. This has important public health implications; in the present study, individuals in the middle quintile of activity performed 7116 steps/d, a level of activity easily attainable by most adults (42).

The strengths of the current study are its large sample size (n = 421) of approximately equal numbers of men (49%) and women (51%), with assessments of key variables performed for 12 mo. These participants exhibited a wide range of physical activity levels, which allowed for a thorough examination of the role of physical activity on appetite and weight gain. We estimated daily physical activity objectively using a validated activity monitor and measured body composition by dual-energy X-ray absorptiometry. We attempted to reduce error associated with self-reported energy intake values by calculating energy intake on the basis of validated mathematical models using objective measures of the other components of the energy balance equation (energy stores, energy expenditure). However, this model and the measurements on which they are based are not without error themselves. This study did not include the measurement of hormones that are related to appetite regulation, which would have allowed for exploration into the biological mechanisms responsible for the results presented here. Finally, the groups examined here were created on the basis of time spent in MVPA; further studies are needed to determine whether the accumulation of light-intensity physical activity has a similar effect on appetite and weight gain.

In conclusion, the results presented here show a positive relation between energy intake and physical activity, with the exception of low levels of activity, expanding similar findings in mice, rats, and humans from decades ago by Jean Mayer. Individuals here who performed the lowest amounts of physical activity had the highest levels of both underreporting of energy intake and levels of disinhibition, in addition to gaining the largest amount of FM over 12 mo of observation. These findings suggest that physical activity plays a role in the regulation of body weight beyond expending energy and in the regulation of appetite, which builds on Mayer’s proposed threshold at which energy intake and expenditure are in balance. The levels of physical activity associated with the prevention of weight gain are moderate and at a level achievable by the majority of adults. We must use this information to develop and implement strategies to prevent and manage the obesity epidemic.