Sedentary behaviors are behaviors performed in seated/lying position requiring low levels of energy expenditure (e.g., <1.5 METs) (19). Americans spend an estimated 55%–70% of waking hours sedentary (17), and even regular exercisers spend large portions of the day engaged in sedentary behaviors (2). Epidemiologic evidence indicates that habitual sedentary behavior and sedentary time independent of physical activity (PA) are associated with a host of poor health outcomes, including increased risk of obesity (9,10), metabolic syndrome (4,22), type 2 diabetes (9,10,12), cardiovascular disease (5,11), and premature mortality (24). Emerging evidence indicates that, in addition to total time spent sedentary, other novel features, such as the number of interruptions (or “breaks”) of sedentary behaviors, may influence physiologic response to habitual sedentary behavior (3,7,8).
Because sedentary behaviors are ubiquitous and spontaneous (16), understanding their physiologic consequences has been challenging. Traditionally, research protocols have used bed rest in humans and hind-limb immobilization in rodents to study how sedentary behaviors affect physiologic outcomes. These investigations indicate that insulin action (23) and lipid metabolism (1,27) negatively respond to sustained sedentary time and suggest that changes to insulin signaling, glucose transport, and lipoprotein lipase activity may govern these consequences (1,27). Although these data offer insights into the physiologic consequences of extreme sedentariness, their generalizability to more typical free-living settings is uncertain, as even the most sedentary but otherwise healthy individuals take breaks from sedentary behaviors to perform basic hygiene and activities of daily living.
Recent research has expanded on bed-rest models by exposing participants to short-term experimental conditions that are more relevant to free-living sedentary pursuits (23). In a controlled laboratory study, Dunstan et al. (3) reported that short (2 min) light-intensity and moderate-intensity interruptions improve postprandial glucose and insulin responses compared to prolonged sedentary time. Similarly, Duvivier et al. (6) reported that exercise does not fully negate the detrimental effects of sitting all day compared to a day of low-intensity ambulatory activities with minimal sitting. These models are more representative of free-living sedentary behaviors than are bed-rest studies; however, the experimental conditions are still not typical of free-living behavior. Ultimately, the goals of this research area are to determine whether there is a causal relationship between sedentary behaviors and poor health outcomes and to establish public health recommendations to decrease sedentary time, if warranted. To do this, the interventions must reflect real-world sedentary behavior, where individuals are free to sit and break from sitting for any amount of time. It is also necessary to expand the intervention duration beyond 24 h.
The primary objective of this study was to investigate free-living individuals’ cardiometabolic response to 7 d of increased sedentary behaviors. A secondary objective of this study was to investigate whether cardiometabolic response could be linked to specific features of habitual sedentary behavior. We used the activPAL™ activity monitor to precisely measure changes in total time sedentary; number, time, and intensity of breaks from sedentary time; break rate (15); step count; and time in sedentary bouts longer than 20, 30, and 60 min, respectively.
The University of Massachusetts institutional review board approved this study. All participants completed a health history questionnaire and an informed consent document approved by the University of Massachusetts institutional review board.
Recruitment and Eligibility
Men and women age 18–35 yr were recruited for this study. Eligible participants were in good physical health (no diagnosed cardiovascular, pulmonary, metabolic, joint, or chronic diseases) and were currently participating in at least 150 min of moderate-intensity activity per week.
Participants reported to the Physical Activity and Health Laboratory after a 12-h overnight fast. Using a standard floor stadiometer and a standard physicians’ scale (Detecto, Webb City, MO), we measured height and weight to the nearest 0.25 cm and 0.1 kg, respectively. Participants also completed a short survey asking about their current PA status (PAS). Participants were asked to choose a number that best described their activity in a normal week. Possible responses ranged from 0 to 7, with 0 corresponding to “avoided walking or exertion (e.g., always used the elevator, drove whenever possible instead of walking)” and with 7 corresponding to “ran more than 10 miles/wk or spent over 3 h/wk in comparable physical activity.” To be eligible to continue with the study, participants must have reported a PAS response of 5 or greater (“ran 1 to 5 miles/wk or spent 30 to 60 min/wk in comparable physical activity”). PAS was used as an initial screening tool, and habitual activity levels were later verified using the accelerometer (described later).
At baseline, participants’ PA and sedentary behavior were measured for 7 d. During this time, participants maintained their normal daily activity, including exercise. Accelerometer data from the baseline period were used to verify participants’ self-reported activity levels. If participants did not perform at least 150 min of moderate-intensity activity, they were no longer considered eligible. No potential participants were deemed ineligible after they have worn the accelerometer. Within 24 h of completing the baseline assessment, participants completed a 7-d sedentary condition. Participants were instructed to increase their time in sedentary behaviors as much as possible, to limit time standing and walking, and to refrain from structured exercise, leisure time PA, and occupational PA. Participants were instructed to accumulate no more than 5000 steps per day and wore an Omron pedometer to facilitate compliance. This device is valid for measuring steps per day (21) and has been used to provide individuals referent goals for meeting activity guidelines (26). Other studies have successfully used a prescription to decrease steps to study reduced activity (14,18). However, we also used an accelerometer to precisely measure features of sedentary behavior. Data from the pedometer were not used in analyses but were only used to provide participants real-time feedback on behavior.
Detailed Measurement of Active and Sedentary Behaviors
ActivPAL™ activity monitor (PALTechnologies, Glasgow, Scotland) is a small (2.0 inches × 1.4 inches × 0.3 inches) light (20.1 g) device that uses accelerometer-derived information about thigh position to estimate time spent in different body positions (i.e., sitting/lying, standing, and stepping). Under each condition, participants wore an activPAL™ monitor on their right thigh during waking hours. The device was attached using a nonallergenic adhesive pad and positioned on the midline of the thigh (one third of the distance between the hip and the knee). A time-stamped “event” data file from activPAL™ software (version 5.8.5) was used to identify the duration of each sitting/lying, standing, and stepping bout. The device also estimates intensity by assigning values of 1.25 and 1.4 METs to sitting and standing events, respectively, and uses cadence to estimate METs for stepping events. The event file was converted into second-by-second data, and a customized R (www.r-project.org) program was used to estimate the following 16 metrics:
- MET-hours: sum of METs multiplied by time (in hours).
- Percent time sedentary: sum of minutes spent in sitting/lying events divided by the total minutes that activPAL™ was worn.
- Percent time in light-intensity activity: sum of minutes spent in light-intensity activity divided by the total minutes that activPAL™ was worn. Although activPAL™ assigns a value of 1.4 METs to standing events, we considered all standing events as light-intensity activity (e.g., in order to be considered sedentary, a seated or lying posture was required).
- Percent time in moderate to vigorous PA (MVPA): sum of minutes spent in MVPA divided by the total minutes that activPAL™ was worn.
- Percent time active: sum of minutes spent in either light-intensity PA or MVPA divided by the total minutes that activPAL™ was worn.
- Time sedentary (in minutes): sum of minutes spent in sitting/lying events.
- Time in light-intensity activity (in minutes): sum of minutes spent in light-intensity activity (1.5–2.99 METs). Although activPAL™ assigns a value of 1.4 METs to standing events, we considered all standing events as light-intensity activity (e.g., in order to be considered sedentary, a seated or lying posture was required).
- Time in MVPA (in minutes): sum of minutes spent in MVPA (≥3 METs). We counted any minute where intensity was ≥3 METs (i.e., activity did not have to be performed in bouts).
- Time active (in minutes): sum of minutes spent in light-intensity activity (1.5–2.99 METs) or MVPA (≥3 METs) (total light time + total time in MVPA).
- Guideline minutes (in minutes): sum of minutes spent in bouts of activity that qualify toward meeting PA guidelines (activity of at least moderate intensity (≥3 METs) and lasting at least 10 consecutive minutes).
- Breaks (count): number of times a sitting/lying event was followed by a standing or stepping event.
- Break rate (number of breaks per sedentary hour): number of breaks per sedentary hour (15).
- Step count (count): cumulative number of steps taken.
- Time in sedentary bouts longer than 20 min (in minutes): sum of minutes spent in sitting bouts that are at least 20 min in duration. Any time an individual interrupted a sitting event with a standing or stepping event of any length, the duration of the sitting bout was ended.
- Time in sedentary bouts longer than 30 min (in minutes): sum of minutes spent in sitting bouts that are at least 30 min in duration. Any time an individual interrupted a sitting event with a standing or stepping event of any length, the duration of the sitting bout was ended.
- Time in sedentary bouts longer than 60 min (in minutes): sum of minutes spent in sitting bouts that are at least 60 min in duration. Any time an individual interrupted a sitting event with a standing or stepping event of any length, the duration of the sitting bout was ended.
During the baseline period and the sedentary condition, participants were asked to maintain their normal dietary habits. On the days before assessment of cardiometabolic outcome variables, participants were asked to keep their diets as identical as possible. This was verified by having participants complete a 24-h dietary recall (ASA24; http://riskfactor.cancer.gov/tools/instruments/asa24/). The ASA24, or automated self-administered recall, is a Web-based tool that enables participant-administered 24-h recalls and is freely available through the National Cancer Institute.
Markers of Cardiometabolic Health
Oral glucose tolerance test
After baseline assessment and sedentary condition, participants reported to the laboratory after a 12-h overnight fast. A catheter was inserted into a forearm vein; fasting blood samples were taken, and a standard 2-h oral glucose tolerance test (OGTT) was performed. Subjects ingested 75 g of glucose (Sun Dex; Fisher Healthcare, Houston, TX) within 5 min, and blood samples were collected every 30 min (0, 30, 60, 90, and 120 min) for the next 2 h. Samples were centrifuged (3000g) immediately for 15 min, and plasma was aliquoted into polystyrene tubes and stored at −80°C until analysis. The following variables were used as markers of cardiometabolic health:
- Fasting lipids: fasting triglycerides (TG) and cholesterol (total cholesterol, HDL, and LDL) samples were collected in sterile syringes and transferred to serum Vacutainers for analysis. Plasma TG were determined using an enzymatic colorimetric assay kit (Sigma Chemical, St. Louis, MO); total cholesterol and HDL were analyzed using the cholesterol oxidase method (Analox Instruments, Lunenburg, MA). LDL was calculated from measured TG, total cholesterol, and HDL levels (LDL = total cholesterol − (TG/5 + HDL)).
- Fasting and 2-h glucose concentrations: glucose concentrations were determined using the glucose oxidase method (GL5 Analox Analyzer; Analox Instruments).
- Fasting and 2-h insulin concentrations: insulin concentrations were determined using a radioimmunoassay kit (Millipore Corporation, Chicago, IL) specific for human insulin. Higher insulin concentrations suggest reduced peripheral insulin sensitivity, as more insulin is needed to dispose of the same concentration of glucose.
- Area under the glucose curve (glucose-AUC): glucose concentrations from five time points were used to calculate glucose-AUC using the trapezoidal method.
- Area under the insulin curve (insulin-AUC): insulin concentrations from five time points were used to calculate insulin-AUC using the trapezoidal method. Although data are limited, insulin-AUC has been associated with all-cause mortality and cardiovascular disease mortality (20,21). Similar to fasting and 2-h insulin concentrations, a higher insulin-AUC in response to OGTT suggests reduced peripheral insulin sensitivity, as more insulin is needed to dispose of the same concentration of glucose.
- Matsuda index: insulin action was estimated using the whole-body insulin sensitivity index (
- ) established by Matsuda and Defronzo (16) (composite insulin sensitivity index (C-ISI)). C-ISI represents a composite of hepatic and peripheral tissues, considers insulin sensitivity in the basal state after a carbohydrate load, and is strongly correlated (r = 0.73) with the direct measure of insulin sensitivity derived from a hyperinsulinemic–euglycemic clamp (16).
Data Cleaning and Reduction
ActivPAL™ data were downloaded and exported to csv files. All data cleaning and processing were performed using the statistics package and computing language R. Wear time was determined from detailed monitor logs that participants completed daily. Participants recorded the time the monitor was put on in the morning after waking, the time the monitor was removed at night before bed, any time that the monitor was removed during the day, and the reason why the monitor was removed (e.g., shower). All nonwear time and sleep time were removed from the accelerometer file before analysis. At least 10 h of activPAL™ data was required for a day to be considered valid, and at least four valid days (including one weekend day) was required for the condition to be considered valid (25).
Sample Size Calculations and Statistical Evaluation
Statistical analyses were performed using R software programs, and significance was set at P < 0.05. Sample size calculations were based on an expected 23.4% change in insulin-AUC after increased sedentary time and a 23.6% estimate of within-person variability (17), assuming a correlation coefficient of 0.6 between repeated measures of outcomes. With 10 participants, we estimated approximately 90% power to detect effect size between repeated measures. Repeated-measures linear mixed models with likelihood ratio testing were used to evaluate the changes in activPAL™ variables and markers of cardiometabolic health from baseline to sedentary condition (primary objective). Linear regression models were fitted to evaluate the relationship between change in cardiometabolic variables and activPAL™ variables (secondary objective).
Ten participants (four men and six women) completed the study. Participants were relatively young (mean ± SD age, 25.2 ± 5.7 yr) and lean (mean ± SD body mass index (BMI), 24.9 ± 4.3 kg·m−2) (Table 1).
Activity and Sedentary Behavior Variables
Under the sedentary condition, participants significantly decreased MET-hours (mean Δ, −3.5 MET·h; 95% CI, −4.9 to −2.5), time in MVPA (mean Δ, −52.7 min; 95% CI, −65.8 to −39.6), time in light-intensity activity (mean Δ, −87.6 min; 95% CI, −120.5 to −54.8), and guideline minutes (mean Δ, −41.7 min; 95% CI, −54.4 to−29.1). Total sedentary time (mean Δ, 14.9%; 95% CI, 10.2 to 19.6) and time in sedentary bouts longer than 20, 30, and 60 min significantly increased, whereas the rate of breaks from sedentary time was significantly reduced (Table 2). According to both the activPAL monitor and the Omron pedometer, step count significantly decreased after the sedentary condition (activPAL: mean Δ, −6850.2; 95% CI, −8578.3 to−5122.1; Omron: mean Δ, −6522.9; 95% CI, −8042.1 to −5003.8). Although the decrease in Omron pedometer step count was slightly less, the Omron pedometer estimate was not significantly different from the activPAL estimate. The purpose of the Omron pedometer was only to facilitate condition compliance by providing participants real-time feedback on behavior; thus, all statistical analyses and step data presented in tables and discussed are from the activPAL monitor. Activity and sedentary behavior variables for the baseline period and the sedentary condition are reported in Table 2.
According to diet recalls, total energy [baseline (mean, 1672.7; 95% CI, 1214.6–2130.8); sedentary (mean, 1671.8; 95% CI, 1146.5–2197.0)] and macronutrient content [baseline: CHO (mean, 45.9%; 95% CI, 34.0–57.9), fat (mean, 35.5%; 95% CI, 22.5–48.5), protein (mean, 18.5%; 95% CI, 10.2–26.9); sedentary: CHO (mean, 40.7%; 95% CI, 27.3–54.2), fat (mean, 39.2%; 95% CI, 25.2 –53.3), protein (mean, 20.0%; 95% CI, 12.1–27.9) ] did not differ during the baseline period and the sedentary condition.
Markers of Cardiometabolic Health
Glucose and insulin response
After the sedentary condition, fasting glucose and insulin concentrations did not change from baseline. Glucose concentrations also remained stable in response to glucose load (OGTT). Conversely, 2-h plasma insulin (mean Δ, 38.8 μU·mL−1; 95% CI, 10.9–66.8) and insulin-AUC (mean Δ, 3074.1 μU·mL−1 per 120 min; 95% CI, 526.0–5622.3) were significantly elevated in response to glucose load (Fig. 1), suggesting that more insulin was needed to dispose of the same amount of glucose. This resulted in a significant 17.2% decrease in C-ISI. Cardiometabolic variables for the baseline period and the sedentary condition are reported in Table 2 and illustrated in Figure 1.
Body mass, BMI, waist circumference, and fasting lipids
There were no significant differences in any fasting lipid (TG, total cholesterol, HDL, and LDL) values after the sedentary condition. Body mass, BMI, and waist circumference did not change from baseline to postsedentary condition (Table 2).
Linear regression was used to evaluate the association between change in activity and sedentary behavior variables and change in insulin action. Because 2-h plasma insulin, insulin-AUC, and C-ISI were the only cardiometabolic variables to significantly change after the sedentary condition, data are presented for these variables only. Change in 2-h plasma insulin was negatively associated with change in percent time in light-intensity activity (r = −0.62, P < 0.05) and positively associated with change in time in sedentary bouts longer than 30 min (r = 0.82, P < 0.01) and 60 min (r = 0.83, P < 0.01). When change in time in MVPA was included in the models, the significant associations of time in sedentary bouts longer than 30 and 60 min persisted (P < 0.05), whereas the association with percent time in light-intensity activity was slightly attenuated (P = 0.09). Although not significant, changes in total sedentary time (r = 0.57, P = 0.09) and break rate (−0.57, P = 0.09) were moderately correlated with change in 2-h plasma insulin. Changes in insulin-AUC and C-ISI were not associated with any activity or sedentary behavior variable (Table 3).
The primary objective of this study was to evaluate the cardiometabolic effects of an acute increase in free-living sedentary behavior (7-d treatment). Consistent with more extreme models of sedentary behavior (i.e., bed rest), when moderately active individuals increase sedentary time, insulin action is significantly reduced. We also identified that this decrease in insulin action was associated with a decrease in percent time in light-intensity activity and an increase in time in prolonged sedentary bouts (>30 min and >60 min). To our knowledge, this is the first intervention performed to measure specific features of sedentary behavior and their effects on cardiometabolic outcomes among free-living individuals.
Free-living model of sedentary behavior
It is well accepted that detraining and extreme sedentary behavior cause significant reductions in insulin action in both animal and human models (27). Stephens et al. (23) reported that after 1 d of sustained sitting, insulin action was reduced by 39%. In this study, participants were confined to a wheel chair for more than 98% of the waking day. They were allowed to fidget and use their arms ad libitum but were not allowed to take breaks from sitting. Consistent with Stephens et al. (23), we observed a 34.5% increase in insulin-AUC. Importantly, our findings were observed using an ecologically valid design. In the present study, participants were prohibited from exercising and encouraged to sit as much as possible for 7 d but were allowed to take breaks from sedentary behaviors and to accumulate small amounts of light-intensity, moderate-intensity, and vigorous-intensity activities as dictated by their natural environment. This protocol resulted in participants spending a mean 76.2% (95% CI, 74.2–78.3) of the day sedentary and accumulating a mean 4308 (95% CI, 3868–4749) steps per day under the sedentary condition. These characteristics are consistent with population surveillance data (7,8,17,21,22) and support the notion that our model of sedentary behavior was similar to behavior typical of a habitually sedentary population.
A longer intervention period may have resulted in larger and/or additional (e.g., negative changes in fasting lipids) metabolic consequences, but our results support emerging experimental evidence (3,6,23) that reduced insulin action may be an initial response to chronic sedentary behavior.
Detailed measurement of active and sedentary behaviors
Measuring free-living sedentary behaviors has traditionally been very difficult. Self-report (questionnaires, interviews, diaries, etc.) consistently underestimates time spent sedentary, and it has been shown repeatedly that waist-worn activity monitors are biased and imprecise in characterizing features of sedentary behavior (13,15). In the current study, we used an activity monitor that was specifically designed to identify posture based on thigh position. With this device, we were able to capture details of sedentary behavior that are traditionally overlooked (e.g., breaks, duration of sedentary bouts). This led to novel findings indicating that less time in light-intensity activity and more time in prolonged sitting bouts greater 30 and 60 min are directly related to increased 2-h plasma insulin. These results complement those by Dunstan et al. (3), who reported that interrupting sitting time with structured bouts of light-intensity or moderate-intensity activity had positive effects on postprandial glucose and insulin responses. Although decreases in step count, time in MVPA, and total time active (time in light-intensity activity + time in MVPA) were not associated with decreased insulin action in the current study, more work is needed to distinguish the specific effects of time in prolonged sitting bouts versus the type (e.g., stand vs walk) and intensity (e.g., light-intensity activity vs MVPA) of the activity performed during the interruption. Nonetheless, our results highlight the importance of simultaneously measuring and studying specific features of sedentary and active behaviors in relation to health.
Two recent studies examined the effects of reduced steps per day on markers of cardiometabolic health in free-living people. After just 14 and 3 d, significant (mean ± SD) reductions in insulin action were observed when healthy active volunteers reduced steps per day from 10,501 ± 808 to 1344 ± 33 and from 12,956 ± 769 to 4319 ± 256, respectively (14,18). These carefully designed studies advanced the field of sedentary behavior research by studying participants in free-living settings rather than using controlled laboratory models (e.g., bed rest, immobilization) and by using objective devices to measure exposure. The current study extends this work by using the activPAL™ monitor to directly examine whether changes in detailed features of PA and sedentary behavior are associated with changes in health outcomes. Future studies employing objective measurement approaches will likely reveal additional features of active and sedentary behaviors that are important to health (e.g., stepping cadence, temporal features). Although beyond the scope of this article, these approaches will also allow for a careful examination of the association among PA and sedentary behavior variables and for investigations into how individuals choose to reallocate time when given a prescription to change sedentary behavior. Understanding how simultaneous changes in PA and sedentary behavior variables interact to impact health is a complex issue with significant implications for public health recommendations. Future studies that use direct objective measurement techniques will continue to elucidate these relationships and will provide the evidence base for public health messages regarding sedentary behavior.
Strengths and limitations
Important strengths of this study include the within-subject design, the use of a free-living setting, and the detailed measurement of multiple features of sedentary behavior using a validated device designed to differentiate sitting and standing behaviors. Controlled laboratory studies have revealed important consequences of sustained sedentary behaviors. The current study expands this evidence through a free-living intervention that allowed for the simultaneous evaluation of important activity and sedentary behavior variables. This type of design has only recently been made possible through improvements in the objective measurement of free-living behavior.
The major limitation of this study is our small homogenous sample. Despite our small sample, we were able to identify important relationships between distinct sedentary behavior variables and reduced insulin action. However, future work is needed to confirm the current results and to uncover additional associations in larger, more diverse groups. For example, it may initially seem surprising that an independent association between time in MVPA and insulin action was not observed, but this may be due to the lack of interindividual differences in how time in MVPA changed from baseline to the sedentary condition. Participants were relatively young, healthy, and active. Additional work is needed to evaluate the potential influences of age, sex, BMI, activity status, and health status. A secondary limitation of our study is that we did not control energy intake. Although energy state impacts the effects of increased sitting (23), any energy imbalance in the current study was minimal, as evidenced by no change in body weight and unlikely significantly impacted outcomes. In addition, recall data indicated that participant diets were very similar on the days immediately before each OGTT and cardiometabolic assessment. Nonetheless, future mechanistic studies would benefit from controlling and measuring energy intake. Lastly, in this study, participants were instructed to refrain from structured exercise and leisure time PA under the sedentary condition. Future work should address how increasing sedentary time, while maintaining structured exercise, impacts cardiometabolic outcomes.
This study provides further evidence that markers of insulin action are negatively affected when active individuals replace active time with sedentary time. The primary contribution of this study is that these results were observed using a novel free-living model of sedentary behavior, where participants performed intermittent bouts of ambulatory activity characteristic of typical sedentary behavior and active and sedentary behaviors were precisely measured using an objective monitoring tool. Advances in objective monitoring tools enable precise measurement of characteristics of sedentary behavior in free-living experimental models. We anticipate that these tools will continue to expose characteristics of sedentary and active behaviors that are important in disease initiation and development.
We would like to acknowledge the members of the Physical Activity and Health and Energy Metabolism Laboratories at the University of Massachusetts, especially Amanda Libertine, Natalia Petruski, and Richard Viskochil. We would like to thank all of the participants who volunteered for this study. We also acknowledge the National Institutes of Health as our funding source.
This work was funded by National Institutes of Health grant RC1HL099557
All authors declare no conflicts of interest.
Current address for Sarah Kozey Keadle: Cancer Prevention Fellowship Program, National Cancer Institute, Bethesda, MD.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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