Increased risk of developing cardiovascular disease and diabetes (1) and elevated biomarkers of risk for these chronic diseases (2) have been observed with high volumes of sitting time, and especially sitting time accrued in a prolonged, continuous manner. Supporting the epidemiological evidence, laboratory studies have shown acute benefits to glucose, insulin, and lipid metabolism of interspersing long periods of sitting with even small amounts of activity (3–5). Accordingly, interventions to reduce sitting, especially in the workplace—a key setting for addressing prolonged sitting time—have been advocated as a public health strategy (6,7). In particular, sit–stand workstations have emerged as effective tools in multicomponent workplace sitting interventions (8) as their usage reduces sitting time by large volumes.
By contrast, with the clear evidence that such interventions can reduce sitting time, the evidence concerning whether they are likely to impart nonacute benefits to cardiometabolic health is less clear, especially when sitting is primarily replaced with standing. Workplace sitting reduction interventions that primarily increase standing (e.g., through installation of sit–stand desks) have shown benefits concerning lipid and glucose biomarkers, but inconsistently (9–11). Notably, thus far, only the sitting reduction interventions that have increased stepping (e.g., by use of treadmill desks) have shown significant benefits to body weight or body composition (12,13). The short-term evaluations and insufficient sample sizes of most studies may explain the mixed findings. However, it is also possible that the potential cardiometabolic benefits of reducing sitting in an intervention are inherently variable because participants can make a plethora of different behavior changes when reducing sitting. Potentially relevant considerations include the volume of sitting reduction, the activities replacing sitting (e.g., standing vs ambulatory activities), and any compensatory activity changes that may or may not occur (14).
Recently, compositional data analysis (CoDA) has been used to simultaneously examine all activities occupying a 24-h day and test them in relation to cardiometabolic biomarkers (15). The study findings revealed that some biomarkers, notably those pertaining to glucose metabolism, improve significantly when increasing light activity at the expense of sedentary time (15). Importantly, CoDA is a valid method for examining data that sum to a fixed total, such as 24 h (15), and it can be applied to evaluate all of the changes in activity that occur during an intervention simultaneously and test these in relation to changes in cardiometabolic biomarkers. To our knowledge, CoDA has not been applied in this context or in the examination of standing as a separate component from ambulatory light activities. Using CoDA, we therefore examined the associations of short- and long-term (3- and 12-month) changes in daily time use with concurrent changes in cardiometabolic biomarkers, within participants receiving the Stand Up Victoria intervention.
METHODS
The Stand Up Victoria cluster-randomized trial was registered with the Australian New Zealand Clinical Trials register (ACTRN12611000742976). The Alfred Health Human Ethics Committee (Melbourne, Australia) granted ethical approval. Participants provided written consent. The study was conducted in accordance with the CONSORT guidelines for cluster-randomized trials (http://www.consort-statement.org/). Details are published elsewhere concerning the study protocol (16), the measures used, the development and pilot testing (10,17), the evaluation of the main activity outcomes (18), and the secondary cardiometabolic biomarker outcomes (19).
Setting and Participants
Teams from study worksites that were at least 1 km apart were identified and recruited from a single organization and then were randomized to the intervention (n = 7 sites, n = 136 workers) or control (n = 7 sites, n = 95 workers) condition. Eligibility criteria for individual participants in the selected teams were as follows: 18–65 yr of age, not pregnant, ambulatory, speaks English, capable of standing or sitting for ≥10 min continuously, and working ≥0.6 full-time equivalent with designated access to a telephone, Internet, and desk. Participants and study staff were not blinded to group allocation. The present study evaluates only the intervention participants.
Intervention
The Stand Up Victoria intervention consisted of organizational support (senior management support, a team champion who sent e-mails containing the intervention messages), environmental modification (sit–stand workstations), and individual health coaching (including goal setting and tracking). The intervention was tapered over 12 months with intensive components (e.g., health coaching and team champion e-mails) ceasing after 3 months. It primarily targeted reductions in workplace sitting time, especially sitting accrued for ≥30 min at a time continuously. The main message was to “Stand Up, Sit Less, Move More.” The intervention encouraged participants to replace part of their sitting across the entire day with standing and stepping, by standing at their workstation for at least an hour a day, and by using a variety of self-selected strategies, which might target standing, stepping or both. Evaluation of the study’s activity outcomes previously revealed that, net of control, the intervention on average produced moderately large effects on reduced sitting and increased standing (≈80 min·d−1 at 3 months and ≈40 min·d−1 at 12 months) with no significant effect on stepping (−6 min·d−1 at 12 months) (18). These effects were established across the entire waking day (i.e., at work and outside of work, considering the entire week rather than just workdays). Cardiometabolic biomarker outcomes, net of control, showed a significant improvement in overall cardiometabolic risk (CMR) and fasting glucose at 12 months and nonsignificant (but typically favorable) effects on the other biomarkers (19).
Data Collection and Measures
Measurements were at baseline, 3 months into the intervention (upon completion of the individual-level health coaching and champion e-mails) and at 12 months, and included an onsite assessment of biomarkers and an activity monitoring assessment. Further participant characteristics were assessed using online questionnaires (LimeService: www.limeservice.com).
Cardiometabolic biomarker outcomes
The collection of these biomarkers is described in detail elsewhere (19), along with their changes over the course of the intervention. The cardiometabolic biomarkers examined were as follows: systolic blood pressure, diastolic blood pressure, weight, fat mass (kg, percent of bodyweight), waist circumference, fasting triglycerides, HDL and LDL cholesterol, total/HDL cholesterol ratio, glucose, insulin, insulin sensitivity (%S), and steady state beta cell function (%B) as calculated using the Homeostatic Model Assessment Model 2 (HOMA2) online calculator (https://www.dtu.ox.ac.uk/homacalculator/) version 2.2.3 and an overall CMR score. CMR scores (20) were calculated by first log10 transforming and normalizing (mean/SD) the relevant biomarkers and then by taking a weighted average of their values:1/5 × waist circumference + 1/5 × triglycerides − 1/5 × HDL cholesterol + 1/5 × fasting glucose + 1/5 × mean of systolic and diastolic blood pressure. Changes in the biomarkers were calculated as follow-up score minus baseline score.
Activity measures
Activity was measured by the highly accurate (21) and responsive (22) activPAL3TM activity monitor (PAL Technologies Limited, Glasgow, UK; minimum version 6.3.0). The waterproofed monitor was secured onto the right anterior thigh with a hypoallergenic patch at the onsite assessment. Each participant was asked to wear the monitor continuously (24 h·d−1) for the following 7 d and to record the following times daily in a diary: starting and finishing work, waking up, going to sleep (“lights out”), and removing and reattaching the monitor. Monitor data were processed as reported in the primary outcomes article (18). Although daily activities can be classified in many ways, we subdivided time use by activity classifications consistent with the intervention and measurement tool: sitting, standing, and stepping (during waking hours, while wearing the monitor) and “other” time (non–wear time and time in bed).
Statistical Analyses
Analyses were performed in STATA version 13 (STATACorp, College Station, TX) and R version 3.3.0, using the packages “compositions” (“acomp” framework), “nlme,” and “lsmeans.” Statistical significance was set at P < 0.05, two-tailed. Missing data were excluded.
Quantifying activity and activity change compositionally
We used compositional methods, which have been outlined as applied to cross-sectional physical activity and sedentary behavior data by Chastin et al. (15). The total 24-h day was divided across four activities (stepping, standing, sitting, and “other”). Sleep, other time in bed, and non–wear time comprised “other” time (i.e., 24 h minus monitored waking hours). CoDA’s property of “subcompositional coherence” means that the exclusion of irrelevant activities does not adversely affect results (23). The analysis includes only the subcomposition of activities that comprise waking hours (stepping, standing, and sitting), i.e., the composition of waking hours. “Other” time was excluded to reduce the number of dimensions and provide efficient estimates. This decision seemed to be reasonable because the “other” time was not targeted by the intervention and did not change much over time at the group level or for individuals. At baseline, 3 months, and 12 months, compositions were calculated using the R function “acomp.” No method was required to address the problem of zero time use, as all participants spent some time in every time use category at each assessment. Compositional changes [StepΔ, StandΔ, SitΔ] were then measured by Aitchison’s perturbation method (23,24). The ratios of each component in the composition or subcomposition, such as [Step12M/StepBL, Stand12M/StandBL, Sit12M/SitBL] for 12-month changes from baseline, were calculated and were then divided by the sum of these ratios. An equal composition of these three activities at baseline and follow-up would result in a compositional change of [1/3,1/3,1/3]. Compositional changes were plotted as ternary diagrams (Fig. 1), with some guide values marked as follows: no change; average sitting reduces by 1-h/16-h day replaced with all stepping, all standing, or half of each; and the average sitting reduces by 2-h/16-h day replaced entirely with standing.
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FIGURE 1: Change in the composition of the waking day between baseline and 12 months. The center shows no change, and each corner is a complete change in the activity (from 0% to 100% of the waking day).
Quantifying associations of activity changes with biomarker changes
The associations of activity changes with biomarker changes were examined as mixed models (“lme” function), with a random intercept for cluster, and fixed effects for changes in the activity composition [StepΔ, StandΔ, SitΔ]. Short- and long-term changes were examined separately. Briefly, we used an isometric log-ratio transformation (i.e., “ilr” function) to measure the compositional change as two parameters (z1 and z2). These parameters are orthogonal and can therefore be safely included together as independent variables in the mixed models (15,23). The isometric log-ratio transformation can be performed from several perspectives. The primary perspective we used allows for the effect of a decrease in the parameter z1 on biomarkers to indicate the effects of making sitting a smaller proportion of the waking day. These effects are estimated while controlling for shifts in the remaining non–sitting time between standing and stepping, the effect of which is measured as the parameter z2. The transformation was as follows:
In addition, we presented selected estimates for the z2 parameter calculated from different perspectives that indicate the effects of shifts in non–stepping time between sitting and standing (more standing less sitting) and the shifts in non–standing time between sitting and stepping (more stepping less sitting). Although the direction and significance of the parameters can be used to understand the findings, the clinical relevance of the coefficients is not straightforward. Estimates were presented partially standardized, with biomarker changes all expressed as several baseline SD values so that the relative effects on the different biomarkers can be compared. To better understand the results, tertiles of predicted improvement (most improved/least worsened to least improved/most worsened) were plotted across changes in the composition that participants made (as presented in Fig. 2). Also, to better indicate effect sizes, the predicted mean improvement was calculated across a range of standing and stepping changes in the composition that culminate in reducing sitting to recommended levels of 50% (25). Consistent using CoDA methods, our analyses did not adjust for total waking hours (or wear time). Instead, a sensitivity analysis using the composition of all 24 h was conducted to verify that excluding changes in “other” time was reasonable (and by implication that ignoring the total amount of waking hours was reasonable).
FIGURE 2: Predicted improvement in overall CMR score (A, left) and insulin (B, right) by changes in the waking day’s composition (12 months).
RESULTS
Baseline characteristics of intervention participants are shown in Supplemental Table 1 [see Table, Supplemental Digital Content 1, Baseline characteristics of the Stand Up Victoria study participants (n = 231, 14 teams), intervention (n = 136, 7 teams) and control (n = 95, 7 teams) groups, https://links.lww.com/MSS/B70]. Relevant data on short- and long-term changes were available from 105 to 120 participants (77%–88%) and 80–97 (59%–71%), respectively. Generally, those who provided data were similar to those who dropped out, except more women than men dropped out during the intervention, which shifted anthropometric biomarkers in directions expected for a group containing more males.
Activity composition
Activity outcomes have been reported previously (18). The mean composition of the 24-h day in sitting, standing, stepping, and “other” time at baseline was 42.9%, 15.8%, 6.9%, and 34.5%, respectively. Considering activity as the composition of daily waking hours, the intervention group’s daily activity was very high in sitting, low in standing, and very low in stepping both at baseline (65.4%, 24.1%, and 10.5%, respectively) and to a lesser extent at 12 months (60.4%, 29.5%, and 10.1%, respectively) (see Figure, Supplemental Digital Content 2, The composition of daily time use at baseline and 12 months, https://links.lww.com/MSS/B71), corresponding to a mean 12-month change of 0.30, 0.39, and 0.31. Figure 1 is a ternary plot of the 12-month changes, with each corner indicating a complete change toward that activity (from 0% to 100% of waking hours) and with the center indicating no change. Individual changes made by participants were highly variable. The mean change in the composition was statistically significant (with the 95% confidence region excluding no change) and was very close to the point indicating a drop in mean baseline sitting of 1 h/16 h awake, when sitting is replaced exclusively with standing.
Changes in the activity composition with changes in biomarkers
Three-month sitting reductions were significantly associated only with changes in systolic blood pressure (P = 0.039), with the direction of associations indicating sitting reduction to be beneficial (Table 1). Long-term (12-month) sitting reductions were significantly associated with improvements in CMR, triglycerides, total/HDL cholesterol ratio, diastolic blood pressure, weight and body fat, waist circumference, and insulin and had a borderline significant (P = 0.063) association with improved insulin sensitivity (Table 2).
TABLE 1: Associations of 3-month changes in the composition of time use across the waking day changes with concurrent changes in overall CMR and biomarkers of lipid metabolism and blood pressure (n = 136 intervention participants).
TABLE 2: Associations of 12-month changes in the composition of time use across the waking day changes with concurrent changes in overall CMR and biomarkers of lipid metabolism and blood pressure (n = 136 intervention participants).
In terms of the forms of sitting reductions associated with biomarker changes, overall CMR scores improved significantly with sitting–standing substitutions (P = 0.031) and with sitting–stepping substitutions (P = 0.028) without a statistically significant difference between standing and stepping (P = 0.240). By contrast, for fasting insulin and insulin sensitivity (HOMA-S), stepping was significantly better than standing as a sitting replacement (P = 0.006 and 0.032). No significant effect on these biomarkers was seen of replacing sitting with standing (P = 0.889 and 0.943), whereas replacing sitting with stepping was associated with significant benefit (P = 0.006 and 0.029). Figure 2 displays the results graphically. CMR improvements were seen when reducing the contribution of sitting to the overall waking day. At some levels of sitting change, there was patterning whereby more CMR improvement was seen when the remaining time use was shifted more toward stepping rather than standing (i.e., from left to right across the graph), but this was not evident with the largest sitting reductions. All of the participants in the most improved tertile of CMR had made sitting reductions. Figure 2B shows that the degree of improvement that occurred at all levels of sitting change appeared dependent on how much of the remaining (non-sitting) time use shifted toward stepping (most beneficial) versus standing.
For the other outcomes that had significantly improved with long-term sitting reduction (i.e., triglycerides, total/HDL cholesterol, diastolic blood pressure, weight, body fat [kg and %], and waist circumference), it was not clear whether these improvements depended on sitting being replaced with ambulatory activities. Suggestive that either standing or ambulation can improve these outcomes, there was no significant difference whether sitting was replaced with standing or stepping. However, the effects on these outcomes observed for replacing sitting with standing did not reach statistical significance, while replacing sitting with stepping was significantly associated with improved total/HDL cholesterol ratio (P = 0.045), diastolic blood pressure (P = 0.027), and fat mass (kg and %, P = 0.034 and 0.022). In addition to statistical significance, the direction of the results and the patterning of biomarker changes across activity as plotted in Supplemental Figures 2–5 are informative (see Figures, Supplemental Digital Content 3, The composition of daily time use at baseline and 12 months, https://links.lww.com/MSS/B72; Supplemental Digital Content 4, Predicted changes in biomarkers of glucose and insulin metabolism by changes activity over 12 months, https://links.lww.com/MSS/B73; Supplemental Digital Content 5, Predicted changes in biomarkers of lipid metabolism by changes in sitting, standing and stepping over 12 months, https://links.lww.com/MSS/B74; and Supplemental Digital Content 6, Predicted changes in body composition by changes in sitting, standing and stepping over 12 months, https://links.lww.com/MSS/B75). These were consistent with these biomarkers improving somewhat by substituting sitting with standing and improving slightly more by substituting sitting with stepping. Table 3 shows the estimated mean 12-month changes in cardiometabolic outcomes when reducing baseline mean sitting (65.4%) to desirable levels (50%) via various replacement strategies. Moderate to strong improvements (0.5–0.8 SD) were seen for many outcomes but only with substantial increases in ambulation. To see a small improvement in mean biomarkers (0.2 SD), only a small percentage of the sitting reduction needed be achieved by increasing ambulatory activities for lipids and blood pressure (20% or less), for insulin (21%), and for some of the adiposity indicators (waist circumference and body fat percentage). The requirement for ambulation was higher for the other outcomes, ranging from 30% to 68% of the sitting replacement.
TABLE 3: Estimated mean 12-month changes in cardiometabolic biomarkers when reducing mean baseline sitting (65.4%) to 50%, with sitting time redirected to standing and stepping in varying proportions.a
Changes in the amount of “other” time relative to sitting standing and stepping were only significantly associated with systolic blood pressure at 12 months, and triglycerides, HDL cholesterol, and HOMA-S at 3 months [see Table, Supplemental Digital Content 7, Associations of changes in the composition of waking hours and all daily time with concurrent 3-month (3M) and 12-month (12M) changes in CMR biomarkers (n = 136 intervention participants), https://links.lww.com/MSS/B76]. For all these outcomes, the conclusions concerning reducing sitting relative to standing and stepping and the shifts between standing and stepping were no different whether examining all hours or only waking hours.
DISCUSSION
Previously, we showed the Stand Up Victoria workplace sitting reduction intervention predominantly reduced sitting by increasing standing (18) and was effective in the long term for improving fasting glucose and an overall CMR score, net of control (19). The present study extends from these findings to understand how the various activity changes that intervention participants made were associated with concurrent biomarker changes, using a novel application of compositional analysis. We found that sitting reduction was associated with significant improvements in the biomarkers of cardiovascular and metabolic health across all of the areas examined (glucose and insulin metabolism, lipid metabolism, blood pressure, and body composition). To varying degrees, the various benefits appeared to depend on the type of sitting reduction (i.e., whether sitting was replaced with standing or with stepping).
Both the previously reported outcomes of the workplace sitting intervention (19) and the present findings may indicate the need for long-term intervention to improve biomarkers via sitting reduction. We saw many significant associations of activity changes with biomarker changes over a 12-month timeframe, and very few over a 3-month period. Although this could be a chance finding, it could also reflect a physiological requirement for long-term behavior change to improve these biomarkers. Either way, it appears prudent to investigate long-term effects rather than infer them from short-term interventions, where benefits may be missed.
Our CMR findings showed that cardiometabolic biomarker improvement can occur when replacing sitting time with nonambulatory activities. However, findings for the individual biomarkers suggested the degree and/or range of cardiometabolic biomarker improvements may be greater when replacing sitting with ambulation than with standing. Fasting insulin and HOMA-S improved significantly more by replacing sitting with stepping than with standing. Some of the findings showed seemingly conflicting results whereby standing was neither significantly beneficial nor significantly inferior to stepping. This apparent conflict is potentially explained by the study’s sample size providing insufficient precision to distinguish standing from either sitting or stepping, with standing having an impact that was more beneficial than sitting but less beneficial than stepping. Larger randomized controlled trial or meta-analyses may yield further insights into the potential benefits of replacing sitting with standing within field-based sitting reduction interventions. Cross sectionally, in isotemporal analyses, reallocating time use away from sitting toward additional standing has shown significant beneficial associations with triglycerides, HDL cholesterol, total/HDL cholesterol ratio, and fasting glucose although not with weight or waist circumference (26). In addition to the outcomes that appear important from the existing literature, our findings suggest that key biomarkers that might be important to collect when evaluating interventions similar to Stand Up Victoria are as follows: those comprising CMR scores, those showing the greatest response to substituting sitting specifically with standing (i.e., waist circumference, fasting glucose, triglycerides, and diastolic blood pressure, whose coefficients for sitting vs standing were largest at ≈0.3 to 0.6 SD), and the biomarkers that showed the most predicted improvement when reducing sitting to desirable levels (25) without large changes to stepping (i.e., lipids, blood pressure, insulin, waist circumference, and body fat).
Consistent with our findings, the underlying biological mechanisms would also tend to suggest that both standing and stepping should be beneficial, but with the greatest benefit for stepping. The added benefit for glycemic control associated with transitions to stepping compared with transitions to standing may reflect greater muscle and/or metabolic activity in general (27,28), or the comparatively higher energy demand associated with activation of fast-twitch glycolytic fibers (29,30). This contrasts with the lesser glycemic benefit of transitions to standing which involve a comparatively lower energy requirement and engagement of oxidative fibers, favoring fat metabolism (29,30). Broadly, the findings aligned with recent acute experimental studies in overweight adults that have sometimes indicated greater improvements in postprandial glucose and insulin responses (3,4,31) by interrupting sitting with intermittent ambulation compared with standing breaks. Similarly, cross-sectional isotemporal analyses have also showed stronger effects on a range of cardiometabolic biomarkers when sitting time is reallocated to additional stepping rather than standing (26). Notably, the component examined as stepping is an amalgamation of various ambulatory activities, and the associated findings are therefore reflective of the “typical mix” of the various ambulatory activities that were performed by the participants of the Stand Up Victoria intervention, which had a predominant focus on light-intensity activity. Among those behaviors grouped as stepping, running likely produces greater benefits than walking slowly, for example. Similarly, effects of sitting are reflective of the “typical mix” of sitting for this population; it is possible that certain types of sitting (e.g., sitting in long bouts, sitting after lunch) are more deleterious than others.
Strengths of the study include the evaluation of the short- and long-term effects on objectively assessed biomarkers alongside accurately and objectively measured behaviors, with good study retention especially in the short term. A novel element was that this intervention that targeted whole-of-day behavior changes was examined with analytic methods suited to such data. A key limitation was that the study was not powered a priori for this secondary analysis and showed evidence of limited power and precision (e.g., the wide margins of error around predicted mean values). We did not adjust for co-occurring changes in the intervention (e.g., in dietary intake) as these are potentially attributable to the intervention; however, the changes may have been coincidental, and therefore our results may be subject to confounding. It appeared unlikely that the findings were strongly affected by unexamined activities or variation in total waking hours. However, this is impossible to verify without accurate and detailed measures (e.g., high-quality sleep, time in bed unable to sleep, etc.) or knowledge of activity during unobserved time. Another limitation was that the study took neither measures of postprandial metabolism nor continuous biomarker measurements in the behavior setting (e.g., by continuous glucose monitoring or 24-h ambulatory blood pressure monitoring). A focus on the postprandial state may be especially important for interventions targeting not only whole-of-day changes but also workplace changes because the postprandial periods after lunch and other meals are often spent at work. Generalizability is limited, as participants were recruited nonrandomly from a single organization, and there was some evidence of a tendency to disproportionately lose women to follow-up. Also, our sample was a general population of workers; effects may also differ within clinical populations.
In conclusion, our study provides further insights into the heterogeneous findings of studies examining the cardiometabolic benefits of reducing sitting time. First, long-term intervention seems necessary to identify relevant changes. Second, if using primarily sitting–standing substitutions, these seemingly need to be large volume and achieved without adversely affecting stepping. Finally, sitting should be replaced with ambulatory activity if benefits to fasting insulin levels are desired and for potentially greater benefits to other biomarkers as well.
The authors thank the following: the Stand Up Victoria study participants, staff involved at the Department of Human Services (particularly Tony Vane and Megan Evans), Peacock Bros for assisting with the logistics, Parneet Sethi for her assistance with data processing, Dr. Takemi Sugiyama and Dr. Sheleigh Lawler for their contribution to questionnaire development, and project field staff (Glen Weisner, Mary Sandilands, Kirsten Marks, Lisa Willenberg, Cameron Johnson, Beth Howard, Stephanie Fletcher, and Michael Wheeler). They also acknowledge the assistance of the Department of Human Services liaison officers Sevasti Athiniotis and Valerie McRorie. This study was funded by the Australian National Health and Medical Research Council (NHMRC) (project grant 1002706), the Victorian Health Promotion Foundation’s Creating Healthy Workplaces program, and the Victorian Government’s Operational Infrastructure Support Program. None of the authors have any conflict of interest or financial disclosures to report, with the exception that Dunstan presented at the JustStand Wellness Summit, a conference organized by Ergotron in 2012 and Healy presented at the same summit in 2013, with travel and accommodation expenses covered by Ergotron for both Dunstan and Healy. Funders had no involvement in the collection, analysis, or interpretation of the data or in the manuscript preparation. The findings are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The views expressed in this article are those of the authors and not necessarily anyone in this acknowledgment list or the American College of Sports Medicine.
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