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Effects of Standing and Light-Intensity Walking and Cycling on 24-h Glucose


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Medicine & Science in Sports & Exercise: December 2016 - Volume 48 - Issue 12 - p 2503-2511
doi: 10.1249/MSS.0000000000001062
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Working adults spend approximately one-half to two-thirds of their working day sitting (27,37), and this prolonged sitting is associated with increased risk of weight gain and obesity (26,31), poor metabolic health (12,19,20), and increased mortality (10,28,40). Although these negative associations between sitting time and adverse health outcomes are largely independent of physical activity (21,22,26), they are most evident in individuals who are physically inactive (35,40). Acute (<7 d) experimental trials have demonstrated that frequent breaks to sitting may attenuate adverse glycemic (11,41,44) and insulinemic (7,11,41,44) responses. Reducing prolonged periods of sitting has therefore emerged as a new focus for reducing risk of cardiometabolic diseases (18,38,39).

A recent review (3) highlighted the beneficial effects of breaking up prolonged sitting with either standing or light-intensity walking on metabolic outcomes, particularly postprandial glycemia. Few studies have directly compared the acute effects of standing breaks and light-intensity activity breaks on postprandial blood glucose. Bailey and Locke (2) reported that interrupting sitting with 2 min of light-intensity walking (2 mph) every 20 min for 5 h reduced postprandial glucose concentrations but that 2-min bouts of standing every 20 min did not. Henson et al. (23) recently demonstrated that breaking up prolonged sitting with 5-min bouts of standing or self-paced, light-intensity walking (~1.9 mph) reduced postprandial glucose, with no differences between conditions. This suggests that standing and walking may be equally effective for reducing postprandial blood glucose provided the breaks from sitting are at least 5 min long.

Although interrupting sitting with short bouts of walking at ≥~2.0 mph (2,11,23,41) can improve glucose metabolism, it is not known if slower walking speeds could have a similar effect. This is relevant when using active workstations because slower speeds (~1.0 mph) may be better for maintaining productivity while simultaneously walking and working (34,45). The workplace has been highlighted as an opportune setting for health promotion (47), and desk-bound employees are considered a key target group for sitting reduction strategies (44).

The extent to which the improved glucose metabolism during walking is due to postural changes alone, or to increases in energy expenditure, is unclear. Cycling workstations can facilitate physical activity in office settings without disrupting work performance or workflow and thus may help desk-bound workers reduce risks associated with sedentary behavior (8,13). Recumbent cycling may offer a suitable alternative to those who do not prefer walking or who might have orthopedic limitations that preclude frequent and/or prolonged periods of walking. Interrupting prolonged sitting with 8 min of moderate-intensity cycling (52% of HR reserve) every hour for 8 h has been reported to reduce postprandial capillary C-peptide levels but had no effect on postprandial glucose (1). We are unaware of any published data on the effects of light-intensity cycling, equivalent in energy expenditure to 1.0 mph walking, on glucose control.

The purpose of this study was to test the independent and combined effects of changes in posture and energy expenditure on 24-h and postprandial interstitial glucose concentrations, assessed by continuous glucose monitoring, among overweight/obese adults. A meta-analysis of prospective studies indicated that elevated nonfasting blood glucose concentration is a risk factor for cardiovascular disease even in apparently healthy adults without diabetes (32), and postprandial hyperglycemia has been reported to better predict cardiovascular disease than fasting blood glucose in normoglycemic adults (9).

We hypothesized that intermittent changes in posture (i.e., standing), increasing energy expenditure (cycling), and the combination of both (walking) would reduce 24-h and postprandial glucose concentrations when compared with uninterrupted sitting. It was further hypothesized that the reduction in glucose would be greater for the increased energy expenditure conditions and that the combination of increased energy expenditure and posture change (walking) would produce the greatest reduction in 24-h and postprandial glucose.



Overweight (body mass index (BMI), ≥25 kg·m−2) or class I obese (BMI, 30 to <35 kg·m−2) men and women, ages 18 to 55 yr, were recruited via e-mail listserves and flyers posted throughout the university community. Because this study examined both blood pressure (48) and interstitial glucose responses, participants had to meet either prehypertension (systolic >120 mm Hg or diastolic >85 mm Hg) or impaired fasting glucose criteria (5.6–6.9 mmol·L−1). In this report, we present glucose results only.

Interested participants were invited to our research facilities for screening on two separate occasions. Three fasting blood glucose measurements using a ReliOn Prime Glucose Monitor (Walmart, Bentonville, AR) and three resting blood pressure measurements using the Oscar 2 ABP System (SunTech Medical, Morrisville, NC) were taken 3 d apart based on the protocol recommendations by the World Health Organization (47). All measurements were taken within 2 wk of the testing period at the research laboratory by trained personnel. The same devices were used for each measurement, and values were averaged to determine eligibility. The intraclass correlation coefficient of our ReliOn blood glucose data show a high degree of reliability (intraclass correlation coefficient = 0.97, 95% confidence interval = 0.93–0.99).

In addition, participants had to be considered insufficiently physically active (<150 min·wk−1 of moderate-intensity physical activity per week). Physical activity level was assessed by the International Physical Activity Questionnaire (42). A prescreening form was completed by interested participants to exclude those who met any of the following exclusion criteria: smoking, pregnancy, known coronary heart disease, orthopedic limitations for performing physical activity, taking medications to control high blood glucose, special dietary requirements, and being advised by a doctor to avoid prolonged periods of sitting. This randomized crossover full-factorial study was approved by the Arizona State University Institutional Review Board, and written informed consent was obtained from participants before participation. The study was registered as a clinical trial at (NCT02616809).


Participants were required to complete the following four conditions in random order: 1) sitting (SIT), 2) standing (STAND), 3) cycling (CYCLE), and 4) walking (WALK) during an 8-h simulated workday. Each condition was performed across four consecutive weeks, 7 d apart. Each condition was designed to elicit a unique stimulus. The SIT condition was considered the control condition (no change in posture and no increase in energy expenditure). The STAND condition elicited a change in posture only (standing time), the CYCLE condition elicited an increase in energy expenditure only (sitting plus cycling), and the WALK condition elicited both an increase in energy expenditure and change in posture (slow walking). Participants were asked to refrain from exercise for 24 h before each of the four testing visits and were provided with a standardized meal the evening before testing. All conditions were performed in the same simulated office environment in our research laboratory. All conditions required an 8-h (0800–1600 h) intervention phase conducted in our research facilities (LAB), a same-day evening postintervention assessment phase from 1600 h until bedtime (EVE), and a sleep phase from bedtime to wake time (SLEEP).

During SIT, participants were asked to remain seated for the 8-h period while performing computer-related tasks (similar to that of a typical office environment). Participants were free to use the restroom before 0850 h, between 1000 and 1030 h, during lunch (1200–1230 h), and between 1400 and 1500 h, but no other physical activity was permitted. Each break was recorded and replicated under each condition. During STAND, participants were asked to stand (using a height-adjustable TrekDesk Treadmill Desk, for a predetermined time each hour as follows: 10 min at 0850 and 0950 h, 15 min at 1045 and 1145 h, 20 min at 1240 and 1320 h, and 30 min at 1400 and 1530 h. This resulted in 2.5 h of accumulated standing time during the 8-h workday and was based on our previous work on ambulatory blood pressure (49), which was another outcome measure in this study (48). Participants alternated between sitting and standing on a nonmoving treadmill (Weslo Cadence G 5.9, Logan, UT) that was placed underneath the TrekDesk Treadmill Desk. During WALK, participants worked at the TrekDesk Treadmill Desk while also walking at 1.0 mph. The CYCLE condition consisted of eliciting movement while seated through periodic bouts of slow pedaling on a Monark cycle ergometer (894e) placed underneath the TrekDesk. The work rate (approximately 20 W) and cadence (25–30 rpm) were adjusted to match intensity and step rate during the WALK condition. The frequency and duration of the walking and cycling bouts were identical with that during STAND. The simulated office environment consisted of a quiet observational room with a one-way mirror. This allowed for continuous observation of participants to ensure protocol adherence. Participants were permitted to continue computer-based work uninterrupted during all conditions with brief exceptions during transitions.

Participants wore an iPro2 continuous glucose monitor (CGM) (Medtronic, Minneapolis, MN) for three consecutive days for each condition, with CGM insertion on the day before testing and removal on the day after testing. Interstitial glucose concentration was recorded at 5-min intervals. All CGM devices were calibrated using the standard finger prick method and glucose meter, four times within each 24-h period under each condition.

Four meals were provided per condition. These included breakfast, lunch, and two dinners (one on the evening before, and one on the test day). Only one option was offered for breakfast, and a selection of microwaveable meals with a choice of snacks were offered for lunch and dinner. Initial meal and snack selections were recorded for each participant and repeated each subsequent week. The energy, macronutrient (in grams and as a percentage of total kilocalories), and fiber contents for each meal were as follows: for breakfast, 479 ± 18 kcal, 84 ± 5 g carbohydrate (70% ± 5%), 10 ± 1 g fat (19% ± 1%), 14 ± 1 g protein (11% ± 1%), and 2 ± 0 g fiber; for lunch, 543 ± 10 kcal, 77 ± 3 g carbohydrate (57% ± 2%), 16 ± 1 g fat (26% ± 1%), 23 ± 2 g protein (17% ± 1%), and 8 ± 2 g fiber; and for dinner, 743 ± 16 kcal, 122 ± 6 g carbohydrate (66% ± 3%), 18 ± 2 g fat (22% ± 2%), 22 ± 4 g protein (12% ± 2%), and 12 ± 2 g fiber. Across the 24-h period, macronutrient content as a percentage of the total kcal consumption was 64% ± 3% carbohydrate, 22% ± 2% fat, and 13% ± 2% protein. Breakfast and lunch were consumed between 0815 and 0845 h and between 1200 and 1230 h, respectively. All meals consumed during the 8-h workday (LAB) were brought directly to the participant (allowing them to remain seated), and participants were advised to consume the dinner meal at the same time for each condition. Participants were informed to avoid caffeine and alcohol during the evening before each condition and during the evening period of the day of each condition.

A Zephyr BioHarnessTM (Annapolis, MD) was worn during the LAB phase to measure HR during all conditions (29). The Zephyr provides real-time HR monitoring, which allowed the matching of HR between WALK and CYCLE conditions via Bluetooth. This was removed at the end of the day and not worn during the EVE and the SLEEP phase.

The activPALTM triaxial physical activity monitor (PAL Technologies Ltd, Glasgow, Scotland) was worn on the right thigh during each condition for 24 h to record time spent sitting, standing, and stepping both while inside and outside the laboratory. This device was used to identify any possible postural compensatory behavior in the evening of the test day that may have occurred as a result of the condition (17).

To measure possible spontaneous changes in physical activity between conditions, each participant wore a GENEActiv (Kimbolton, United Kingdom) accelerometer on his/her nondominant wrist throughout the 5-wk study period (14). Activity counts were accumulated for the 60-s epochs. Data with at least 600 min·d−1 of wear time were included in analyses. Nonwear time, defined as 60 min or more in which the device did not pick up any activity, was excluded from the analysis. Time spent in sedentary, light-, moderate-, and vigorous-intensity physical activities was calculated using published algorithms (15).

Data analyses

CGM data were used to calculate mean interstitial glucose and total area under the curve (AUC) for the entire 24-h period per condition and during each phase (LAB, EVE, and SLEEP). Bedtime and wake time logs were used to categorize data for EVE and SLEEP, which varied per participant. To standardize the duration of EVE and SLEEP phases across conditions for each participant, the average bedtime and wake time was calculated for each participant and used for each of the four conditions. For postprandial analysis, 2-h postprandial incremental AUC (iAUC) was calculated for each meal and for the cumulative 6-h postprandial period. The postprandial mean glucose and iAUC were compared across conditions. AUC for 24-h glucose and iAUC for postprandial glucose were calculated using the trapezoidal method (46). For iAUC calculations, the three preprandial glucose values obtained just before each meal were averaged and used as a preprandial baseline. Glucose values during the 2-h postprandial period that were below the baseline were not included in the iAUC analysis (<5% of all 2-h postprandial measurements). In addition, several indices of glycemic variability were also assessed (24).

Data were assessed for normality and variables with skewed or kurtotic distributions were transformed to achieve normality. Data are expressed as means ± SD. Linear mixed models (LMM) were used to test for differences between treatment conditions for all glucose measurements. Age, gender, BMI, and baseline glucose level were entered as covariates in the model.

LMM were also used to compare HR differences between conditions. Postural changes (activPAL™) during work hours and after work hours and time spent in sedentary, light-, moderate-, and vigorous-intensity physical activities (GENEActiv) were analyzed with ANOVA.

Missing data

To avoid the underestimation of AUC and iAUC due to missing data, we excluded data sets that were less than 80% complete. Only incomplete data were removed. Subject data were not excluded from all other conditions as data were considered “missing at random.” A minimum of six values per condition was required for each iAUC or AUC comparison. This varied per condition (iAUC and AUC) and phase (24-h, LAB, EVE, and SLEEP AUC only). The mean completeness of AUC data (measured as a percentage of the maximum number of values possible per condition; n = 9) per phase (24-h, LAB, EVE, and SLEEP) was ≥75% for each condition: SIT (75% ± 11%), STAND (84% ± 11%), WALK (84% ± 19%), and CYCLE (80% ± 6%). Within each 2-h postprandial period, a maximum of 24 readings could be collected per subject per condition, totaling a maximum of 216 readings per meal. At the subject level, when the 2-h postprandial data were less than 80% complete (less than 20/24 readings) it was removed from the condition given the effect this would have on the calculation of iAUC. Data completeness (calculated as a percentage of the maximum number of values possible per condition, n = 9) for each postprandial period was ≥78% for each condition: SIT (89%), STAND (78%), WALK (89%), and CYCLE (78%). Overall, missing data across conditions were low. Within the 24-h period, a maximum of 288 readings could be collected per subject and 2592 readings per condition. Data completeness (calculated as a percentage of the maximum possible readings) was ≥84% for each condition: SIT (84%), STAND (90%), WALK (91%), and CYCLE (90%).


Ten participants were enrolled in the study. One participant withdrew because of health complications unrelated to the study. Two participants met the criteria for impaired fasting blood glucose; the remaining seven met the prehypertensive criteria. Therefore, nine overweight/obese (BMI = 29 ± 3 kg·m−2) adults (two males, seven females) with mean ± SD age of 30 ± 15 yr completed all four conditions.

Data from the Bioharness, activPAL, and GENEActiv validated the study design (Table 1). HR for SIT and STAND was significantly lower than WALK and CYCLE (P < 0.01). Minutes spent seated, standing, and walking during EVE were not different between conditions (P > 0.05). As expected during the LAB phase, participants spent significantly more time sitting during SIT (420 ± 54 min) and CYCLE (342 ± 77 min) compared with STAND (252 ± 99 min) and WALK (269 ± 27 min) (P < 0.05). Significantly more time spent walking was detected during WALK (146 ± 18 min, P < 0.01) and significantly more time spent standing was detected during STAND (174 ± 36 min, P < 0.01). The GENEActiv data revealed no significant differences in physical activity or sedentary time between conditions across the entire 5-wk study period. Moreover, the analysis of GENEActiv data during the 24 h preceding each of the four conditions also revealed no differences in sedentary time or time spent in light-, moderate-, or vigorous-intensity physical activities (data not shown). Finally, GENEActiv data confirmed that participants were insufficiently active, accumulating an average of only 15–19 min·d−1 of moderate-intensity physical activity and 0–1 min·d−1 of vigorous-intensity physical activity (Table 2).

HR derived from Bioharness data, total time spent seated, standing, or walking during workday hours (LAB), after workday hours (EVE), and during the entire day until bedtime (LAB + EVE) derived from the activPALTM, and average daily time spent in different physical activity domains during each week for each condition derived from the GENEActiv.
Mean interstitial glucose concentration and AUC for SIT, STAND, WALK, and CYCLE during workday hours (LAB), after workday hours (EVE), and SLEEP across the 24-h period and during the postprandial periods (breakfast, lunch, and dinner).

All recorded bedtimes were between 2200 h and midnight (mean = 2318 h) and wake times between 0530 h and 0745 h (mean = 0642 h). Mean within-subject differences in bedtime (42 ± 20 min) and wake time (38 ± 25 min) were not significantly different, and the earliest and the latest bedtime or wake time differed by more than 60 min (75 min, for wake time) for only one participant. For the calculation of glucose AUC, EVE duration averaged 438 ± 35 min and SLEEP duration averaged 444 ± 32 min.

Interstitial glucose

During the 24-h period, and during LAB and EVE phases, mean glucose was 5%–12% lower for STAND, WALK, and CYCLE compared with SIT (Fig. 1 and Table 2; all P < 0.001). The 24-h mean glucose for WALK was also significantly lower than STAND (P < 0.05), and the 24-h mean glucose for CYCLE was significantly lower (4%–12%) than all other conditions (P < 0.001). Mean glucose for CYCLE was also ~4% lower than STAND during LAB (P < 0.05) and ~5% lower than both STAND and WALK during EVE (P < 0.001). During SLEEP, only for CYCLE did glucose remain significantly lower than SIT and was also significantly lower than STAND and WALK (all 9%, P < 0.001).

Mean 24-h interstitial glucose profiles for SIT, STAND, WALK, and CYCLE. The intervals for standing and light-intensity physical activity during the LAB phase are illustrated by shaded areas. Mean bedtime was 2318 h and mean wake time was 0642 h, n = 9. See text and Table 2 for details and statistical comparisons.

Cumulative 6-h postprandial mean glucose was 5%–12% lower for STAND, WALK, and CYCLE compared with SIT (Fig. 2 and Table 2; P < 0.001). Cumulative 6-h postprandial mean glucose was also lower for WALK compared with STAND, and for CYCLE compared with both STAND and WALK (all P < 0.05). The differences in cumulative 6-h postprandial mean glucose were entirely due to differences observed after the breakfast and dinner meals, with CYCLE having the greatest glucose-lowering effect. For the breakfast 2-h postprandial period, mean glucose was lower for STAND, WALK, and CYCLE compared with SIT (all P < 0.001) and for WALK and CYCLE compared with STAND (both P < 0.001). For the dinner 2-h postprandial period, mean glucose was lower for STAND, WALK, and CYCLE compared with SIT (all P < 0.001) and for CYCLE compared with STAND (P < 0.001) and WALK (P < 0.05).

Postprandial mean interstitial glucose profiles for breakfast, lunch, and dinner during each condition (SIT, STAND, WALK, and CYCLE), n = 9. Error bars represent 95% confidence intervals and are displayed only at 20-min intervals to improve clarity. See text and Table 2 for details and statistical comparisons.

For the cumulative 6-h postprandial iAUC, CYCLE was 44% lower compared with SIT (P < 0.001), and WALK was 24% lower compared with SIT (P < 0.05). CYCLE 6-h postprandial iAUC was also 39% lower than STAND (Table 2; P < 0.01).

No measures of glycemic variability (24) were significantly different across conditions (P > 0.05).


In support of our hypothesis, intermittent changes in posture and/or increasing energy expenditure via standing, walking, or cycling at a light-intensity (~2 METs) throughout an 8-h simulated workday reduced 24-h and postprandial glucose concentrations compared with uninterrupted sitting. Compared with SIT, 24-h mean glucose was reduced by approximately 5%–11% after STAND, WALK, and CYCLE, and the cumulative 6-h postprandial glucose iAUC was reduced by 24%–44% after WALK and CYCLE. Although the clinical significance of these reductions in a healthy population is uncertain, elevated nonfasting blood glucose is a risk factor for cardiovascular disease even in apparently healthy adults without diabetes (32), and postprandial hyperglycemia has been reported to better predict cardiovascular disease than fasting blood glucose in normoglycemic adults (9).

It was further hypothesized that the treatment effect would be larger for the increased energy expenditure conditions. This was supported by the finding that 24-h glucose during WALK and CYCLE was lower compared with STAND, and this was validated by the Bioharness data in which greater HR was observed during WALK and CYCLE compared with SIT and STAND. During SLEEP, only the CYCLE condition exhibited a significantly lower glucose compared with SIT. To our knowledge, this is the first study to show that light-intensity activity in the form of multiple bouts of slow cycling during the day exhibits beneficial effects on glucose during sleep time.

We anticipated that a combination of posture change and increasing energy expenditure via walking would produce the most significant reduction in 24-h and postprandial glucose. However, the CYCLE condition demonstrated the most pronounced treatment effect. The greater effect for the CYCLE condition was not expected. We carefully controlled cycling power output and cadence to match energy expenditure and step rate associated with walking at 1.0 mph (6), and Bioharness data confirmed that there was no significant difference in HR between WALK and CYCLE. Therefore, the larger treatment effect produced under the CYCLE condition cannot be attributed to higher energy expenditure than WALK. One possible explanation may be the highly localized muscle activation of the quadriceps, which may promote a higher uptake of glucose than the global muscle activation experienced during walking. Although this could possibly explain the results during the 8 h spent in the laboratory, it is unlikely to explain the EVE and SLEEP phase results because contraction-stimulated glucose uptake is no longer observed within a couple h after exercise (33). Thus, mechanisms related to increased insulin sensitivity may play a greater role during the postwork evening hours and during sleep. Quadriceps GLUT4 activity is significantly correlated with whole-body insulin sensitivity (25), and cycling could be expected to activate quadriceps muscle more than walking. Further research is required to determine why the treatment effect is higher for cycling compared with walking when energy expenditure is not significantly different between treatment conditions. These results may have high relevance to a work-based environment given that pedaling in a seated position may provide the most benefit for glucose control and may be better suited for working while seated (8).

Several recent studies have demonstrated that interrupting sitting with walking or standing reduces postprandial blood glucose (2,3,11,23,41). Walking at ≥2.0 mph (2,11,23) or 60% of maximum oxygen uptake (41) may not be as suitable for working at a walking workstation compared with walking at 1.0 mph (34,45). Our study supports that walking at 1.0 mph (~2 METs) is sufficient to reduce mean and postprandial glucose not only during work hours but during the evening hours after work. Our results also provide insight into the effect of standing on glucose control. Prolonged periods of standing, ranging from three continuous hours in one afternoon (7) to nearly 10 h (combined with light-intensity activity) accumulated for 24 h (43), have been shown to reduce postprandial blood glucose excursions (7) and prevent deterioration of insulin action (43) compared with prolonged periods of uninterrupted sitting. Standing breaks of 30 min every hour for 8 h have also been shown to reduce the postprandial blood glucose response (44). Although 2-min standing breaks every 20 min for 8 h did not reduce postprandial blood glucose (2), it was recently reported that 5-min standing breaks every 30 min for 7.5 h did (23). In our study, the reduced glucose concentration during STAND was already evident in the morning hours, when the duration of each standing break was only 10–15 min each h (Fig. 1). Thus, lesser amounts of standing than previously reported (7,43,44) may be useful for reducing 24-h glucose concentrations and postprandial glucose responses that are observed during uninterrupted sitting.

The mechanisms for the reduced mean glucose during STAND are largely unknown. The gastrocnemius muscle is constantly active during standing (25), and sustained contractile activity in this muscle may facilitate muscle glucose uptake via recruitment of GLUT4 transporters (16,36).

Our study has several strengths. This is the first study to compare the effects of standing and both light-intensity walking and cycling with uninterrupted sitting on 24-h and postprandial glucose. The activPAL and GENEActiv data confirmed planned differences in posture allocation among the four conditions. Furthermore, activPAL and GENEActiv data indicated that any differences between conditions during the measurement periods outside the LAB phase were not due to compensatory behavior during the evening hours. The total number of lower-limb movements during the WALK and CYCLE active periods was controlled by matching cycling cadence to the step rate of walking at 1.0 mph. The use of continuous glucose monitoring allowed us to examine interstitial glucose during the 24-h period. Consequently, our study also provides new information to show that the effects of standing and very light-intensity physical activity on glucose responses extend for several hours beyond the period during which the activities are performed.

The primary weaknesses of our study are the small sample size and the fact that only two of the nine participants had impaired fasting blood glucose. However, apparently healthy adults without diabetes may benefit from lower postprandial (9) and 24-h glucose concentrations (32). Our significant findings on 24-h and postprandial glucose on a small sample of adults with relatively normal glycemia highlight the potential effectiveness of our intervention. The small sample size reduced our ability to detect statistically significant reductions in 24-h glucose AUC that would be expected on the basis of the results for the mean glucose data. Similarly, because of the reduction of multiple data points to one value per AUC and iAUC calculation, incomplete data sets were excluded to avoid the underestimation of AUC and iAUC results. This was required to protect the integrity of the results and further reduced the sample size. A larger sample size may reduce the effects of missing data and may enable the detection of significant AUC and iAUC differences between conditions. By contrast, LMM analyses of glucose data included a greater number of measurements (i.e., every 5 min) per participant for each condition.

We did not control for menstrual cycle phase, and five of our seven female participants were between the ages of 18 and 44 yr. Insulin sensitivity has been shown to change during the menstrual cycle (14), but it has also been reported that menstrual cycle phase had no effect on glucose tolerance or insulin secretion (5). Because several other studies similar to ours have not reported whether they controlled for menstrual cycle phase (1,2,7,11,41), this is a potentially important methodological consideration that needs to be incorporated into the design of future studies (44).

Although the timing and the duration of the breakfast and lunch meals were strictly controlled, the consumption of the dinner meal occurred outside the laboratory. Thus, we were unable to verify subject compliance with instructions for the consumption of the dinner meal. The inspection of CGM data indicated that for a given participant, the dinner meal was consumed at approximately the same time of evening under all conditions.

We acknowledge that there is limited CGM accuracy in comparison with capillary blood glucose measurement (4). However, given the rigor of the experimental design, completing multiple blood draws would have been too burdensome. The standardization of meals was also generalized for all participants and was not adjusted to accommodate differences in resting metabolic rates. However, any potential glycemic effects would have been present under all conditions, and such practices are regularly conducted with OGTT methods in which all subjects are provided with the same glucose solution concentration. This also would not confound any differences between the WALK and the CYCLE conditions for which energy expenditure was matched. The standardization of meals was enforced the evening before each test day, but dietary intake during the entire 24- to 48-h period before each test day was not controlled. Future studies may benefit from the inclusion of subjective measures of appetite and may control dietary intake 24–48 h before each test day to investigate potential confounding effects.

Our experimental design of accumulating 2.5 h of nonsitting time, with progressively longer durations throughout the workday, may prove to be more than needed to lower 24-h glucose. We used this design on the basis of our previous work on ambulatory blood pressure (49), which was another outcome measure in this study (48). Figure 1 shows that during the LAB phase, the greatest reductions in glucose during STAND, WALK, and CYCLE occurred during the first half of the LAB phase, when the durations of nonsitting time were the shortest. It has been documented that accumulating 2-min bouts of light-intensity (2.0 mph) and moderate-intensity (3.6–4.0 mph) walking every 20 min for 5 h has been shown to alter the expression of numerous skeletal muscle genes, including those associated with glucose metabolism (30). It would be useful to determine whether posture change or very light-intensity activity (~2 METs) alters skeletal muscle gene expression.

Investigating the effects of posture change or very light-intensity activity for an extended period would also provide useful data regarding the true effects of sitting as an occupational hazard. Whether there is a dose–response relationship and an optimal duration of posture change and/or light-intensity activity that is both effective and suitable for the workplace environment is yet to be determined.

The authors thank the School of Nutrition and Health Promotion for funding support of this study.

The authors have no conflicts of interest to declare.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.


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