High levels of sitting are associated with increased risk of developing cardiovascular disease and diabetes (37), as well as with elevated risk biomarker levels (17). Reducing sitting time, particularly time accrued in prolonged, unbroken sitting bouts (6), has been proposed as a strategy for enhancing cardiometabolic health (18). One of the primary target settings for reducing prolonged sitting time is the office workplace (16). Office workers spend on average some three quarters of their workday sitting, with much of this time accrued in prolonged, unbroken bouts of 30 min or more (14,28). Several trials have now demonstrated the feasibility and acceptability of reducing workplace sitting time (2,3,11). These workplace sitting-reduction interventions can improve indicators of cardiometabolic health (2,21,22), either through replacing sitting with ambulatory activity (for example, through use of a treadmill desk) (3,21,22,24) or replacing sitting with standing (for example, through use of a sit–stand desk) (2,11,13). However, findings are inconsistent and a clear picture of which aspects of cardiometabolic health may improve and which do not is yet to emerge because the relevant evidence is from studies with small sample sizes, short intervention durations (3 months or less), and/or have used pre–post or nonrandomized study designs (2,10,11,13,21,22,25). To inform occupational and public health policy and practice, rigorous, long-term evidence on the impact of reducing sitting time on biomarkers of cardiovascular and metabolic health is needed.
We previously reported on the primary and secondary activity outcomes of the Stand Up Victoria cluster-randomized workplace intervention (14). This trial, which incorporated organizational, environmental, and individual elements to target reductions in workplace sitting time (7), was effective in reducing sitting time (especially prolonged sitting time), and increasing standing during work hours, as well as overall (i.e., accounting for any improvements, worsening or absence of change outside of work), but there were no statistically significant increases in stepping in the short- or long term (14). This current study reports on the initial (3 months) and longer-term (12 months) impact of the Stand Up Victoria intervention on the cardiometabolic risk biomarker outcomes.
Ethics approval for this cluster randomized controlled trial was granted by Alfred Health Human Ethics Committee (Melbourne, Australia), with prospective trial registration with the Australian New Zealand Clinical Trials register (ACTRN12611000742976). Data were collected from 2012 to 2015 with analyses undertaken 2015 to 2016. The study complies with the Declaration of Helsinki and was conducted in accordance with the CONSORT guidelines for cluster randomized trials (http://www.consort-statement.org/). A detailed study protocol (7), including the properties of the measures used, description of the intervention development process (29), and baseline characteristics by worksite (12) are available; brief details are provided below. Findings for the main activity outcomes showed that the intervention reduced overall sitting time (work and nonwork time) by 77.7 min per 16 h (95% confidence interval [CI], −100.3 to −55.2) at the 3-month assessment and 36.3 min per 16 h (95% CI, −62.6 to −10.0) at the 12 month assessment compared to the controls, with corresponding increases in standing time (14). Intervention effects for overall stepping time at both the 3-month assessment (1.0 [−6.9, 8.8] min per 16 h) and 12-month assessment (−6.0 [−14.2, 2.2] min per 16 h) were small and not statistically significant (14).
Setting and Participants
A large, single, public service organization was recruited to take part in the study. From this organization, geographically separate (≥1 km apart) study worksites were identified, with teams recruited from each worksite, and employees invited from participating teams. Eligibility criteria for individual participants were at least 0.6 full-time equivalent work hours; access to a telephone, internet, and desk within the workplace; age 18 to 65 yr; not pregnant; ambulatory; able to speak English; and able to stand or sit for at least 10 min continuously. All participants provided written informed consent. Participants and study staff were unblinded to group allocation; laboratory staff involved in the biochemistry analyses were blinded.
The control group underwent the same assessment protocol as the intervention group. Participants received written feedback on their activity and biomarker outcomes at 3 and 12 months.
As previously described (7,14,29), the Stand Up Victoria intervention aimed to reduce sitting time across the whole day, primarily targeting workplace sitting time, with a particular emphasis on reducing sitting time accrued in prolonged unbroken bouts of at least 30 min. The intervention was comprised of an organizational component (senior management support, recruitment of a team champion, emails from the team champion promoting the intervention messages), an environmental component (sit–stand workstations), and an individual component (health coaching, goal setting, and tracking). The main intervention message of “Stand Up, Sit Less, Move More” targeted sitting reduction by increasing both standing and stepping. The strategies used to address these targets were self-selected by the participant during the initial face-to-face health coach session, with progress monitored by the health coach in up to four subsequent telephone sessions.
Data Collection and Measures
Assessments, including activity monitoring, an onsite assessment, and an online questionnaire, occurred at baseline, 3 months, and 12 months. Body composition, blood pressure, and fasting blood measures were collected and activity monitors attached during the on-site assessment. Participants were then emailed a link to the self-administered online questionnaire through which sociodemographic, work-related, and other health-related data were collected.
Cardiometabolic risk biomarker outcomes
Cardiometabolic risk biomarker measures were taken as part of the morning on-site assessments. Fasting blood samples (≥8 h) were collected by a trained phlebotomist and sent immediately to an accredited testing laboratory for analyses of: glucose (spectrophotometric-hexokinase method); cholesterol and triglycerides (standard enzymatic-colorimetric methods); and, insulin (electrochemiluminescence immunoassay) using Roche/Hitachi Cobas® system analyzers (Tokyo, Japan). Low-density lipoprotein (LDL) cholesterol was estimated from the measured high-density lipoprotein (HDL) and total cholesterol using the Friedewald equation (9). Waist and hip circumference (nearest 0.1 cm) were measured in duplicate with a third measurement taken if the first two differed by ≥1 cm. Weight (nearest 0.1 kg), fat mass, fat-free mass, and percent body fat were measured using foot-to-foot bioelectrical impedance analysis scales (model TISC-330S; Tanita Inc., Tokyo, Japan) in the fasted and voided state. Height was measured in duplicate to the nearest 0.1 cm, with body mass index (BMI; kg·m−2) calculated using the average height and weight. Resting systolic and diastolic blood pressure were measured in duplicate via a digital blood pressure monitor (OMRON HEM-907; Omron Healthcare, Japan) on the right arm. Participants who attended in a nonfasting state had their anthropometry and blood pressure measured, but were provided referral forms for fasting blood draws at a local pathology testing site.
Cardiometabolic risk biomarker outcomes were as above, with total cholesterol examined as total/HDL cholesterol ratio. Additionally, insulin sensitivity (%S) and steady state beta cell function (%B) were examined, as well as an overall cardiometabolic risk score. This validated score (36) was added to the study after clinical trials registration to indicate the overall benefit (if effects are cumulative), or lack thereof (if effects counteract). The homeostatic model assessment version 2.2.3 (HOMA2) online calculator (https://www.dtu.ox.ac.uk/homacalculator/) was used to measure insulin sensitivity and output mostly from measured glucose and insulin, and occasionally from truncated glucose and insulin values (for out-of-range data). Single measures, particularly of insulin which is pulsatile (30), can register values outside the valid range for the HOMA2 models when the true amount is some other low or high value. Truncation was used to minimize biases from the exclusion of extreme values and reduce the impact of outliers. Cardiometabolic risk scores (36) were calculated by log10 transforming and then normalizing (mean/SD) six biomarkers, then taking a weighted sum of the biomarker values (1 × waist circumference, 1 × triglycerides, −1 × HDL-cholesterol, 1 × fasting glucose, 0.5 × systolic and 0.5 × diastolic blood pressure), and dividing by 5 (the sum of the weights).
Data on participant characteristics at baseline were collected for consideration as potential confounders (see Table, Supplemental Digital Content 1, Variables considered as potential confounders and adjusted in analyses, https://links.lww.com/MSS/A953). Musculoskeletal health was assessed using the Nordic Musculoskeletal Questionnaire (5), with problems considered separately for the lower back, lower extremities and upper extremities (14). Quality of life was assessed as the physical and mental domains of the Assessment of Quality of Life Survey (32). Job control and productivity were assessed using the Health and Work Questionnaire (33); an indicator of mental demands was derived from the Work Limitation Questionnaire (23). Dietary behaviors were assessed using the Fat & Fiber Behavior Questionnaire (31). Moderate- to vigorous-intensity stepping (h/16 h·d−1) was derived from the activPAL activity monitor, as previously described (14). Measures of fatigue, headaches, and sleep quality were also collected (7).
Minimum differences of interest (MDI) for the cardiometabolic outcomes were: 2 kg weight, 1 kg/1.3% body fat; 2 cm waist circumference, 3 mm Hg blood pressure; 5% HDL, LDL and total/HDL cholesterol (i.e., 0.07, 0.16, and 0.18 mmol·L−1); 10% triglycerides (0.12 mmol·L−1); 10% glucose, insulin, HOMA-%B and HOMA-%S (i.e., 0.49 mmol·L−1, 0.73, 8.7, and 14.0 μU·mL−1); and, 0.17 (approximately one third of a standard deviation) for cardiometabolic risk score. The sample size was selected to provide adequate power for the primary activity outcome (workplace sitting time), not the secondary biomarker outcomes (7). The minimum detectable differences with ≥80% power (5% two-tailed significance) (7) indicated the trial was adequately powered to detect the MDI for body composition and glucose but would have required a larger sample to provide adequate power for cholesterol outcomes, blood pressure, and insulin.
Analyses were performed in STATA version 13 (STATACorp LP, College Station, TX) and R version 3.3.0 with statistical significance set at P < 0.05, two-tailed, and reporting any interactions at P < 0.1. For continuous outcomes, intervention effects and changes within groups were estimated using linear mixed models. Outcome variables were log transformed as required, to improve normality and/or reduce heteroscedasticity. Models included group (intervention/control), time (3–12 months), the group–time interaction, baseline values of the outcome and potential confounders as fixed effects, and random intercepts for workplace (REML estimation), and used an unstructured variance-covariance structure for the repeated measures. A list of all potential confounders was first identified a priori; models were adjusted for those associated with the outcome at P < 0.2 in backwards elimination (see Table, Supplemental Digital Content 1, Variables considered as potential confounders and adjusted in analyses, https://links.lww.com/MSS/A953). Only preintervention (baseline) values were considered as potential confounders. Estimates of changes within groups, and differences between groups, were obtained from marginal means and pairwise comparisons of marginal means of either the outcome or predicted values of the outcome back-transformed to the original scale (for transformed outcomes). Overall results across both 3 and 12 months combined were presented only if intervention effects did not differ between these time points at P < 0.1.
Missing data were multiply imputed by chained equations, using m = 20–50 imputations. Imputation models included all variables used in the analysis, a fixed effect for cluster (8), and any variables that showed an association with the odds of missing data at P < 0.2 (see Table, Supplemental Digital Content 2, Odds of missing data [logistic regression] in 136 intervention [Int] and 95 control [C] participants, https://links.lww.com/MSS/A954). No clusters were lost to follow-up. However, due to additional data loss for glucose and insulin from a laboratory error that occurred while processing a particular cluster’s samples, a small control cluster had no participants with available data specifically for the glucose metabolism models. For these models, this cluster was analyzed combined with another control cluster from a similar geographical location. Sensitivity analyses in completers are reported in Supplemental Digital Content 3 and 4 (see Table, Supplemental Digital Content 3, Changes from baseline in glucose metabolism, and differences between intervention and control groups, adjusting for confounders [completers analysis], https://links.lww.com/MSS/A955; and Table, Supplemental Digital Content 4, Changes from baseline in body composition, lipids, and blood pressure, and differences between intervention and control groups, adjusting for confounders [completers analysis], https://links.lww.com/MSS/A956).
As previously reported (14), 278 employees across the 14 sites initially expressed interest in the study, with 231 participants (5–39 per worksite) enrolled and ascertained to be eligible upon completing baseline assessment. Baseline characteristics are shown in Table 1. Most participants were overweight or obese according to their BMI (≥25 kg·m−2; 70.6%) and waist circumference (men, >102 cm; women, >88 cm; 74.9%). Many (86.5%) had levels of at least one blood lipid that were inconsistent with recommendations (34), and 11.7% had diabetes (self-report of a previous diagnosis by a doctor or fasting glucose ≥7 mmol·L−1). The intracluster correlations (ρ) in baseline values of the cardiometabolic outcomes ranged from inestimably small (<0.001) for cardiometabolic risk score, body fat, waist circumference, HDL cholesterol and triglycerides to 0.101 (0.026 to 0.317) for HOMA-%B (see Table, Supplemental Digital Content 5, ICCs [95% CI] for worksite clustering at baseline [n = 14 clusters; n = 231 Stand Up Victoria participants], https://links.lww.com/MSS/A957).
Intervention effects on cardiometabolic risk biomarkers
Intervention effects differed between initial and long-term outcomes at P < 0.1 for fasting glucose (P = 0.054), insulin (P = 0.002) and HOMA2-%S (P = 0.003) only. Thus, intervention effects are shown separately for initial and long-term outcomes for overall cardiometabolic risk (of which glucose was a component), and biomarkers of glucose and insulin metabolism, and are shown pooled for the other outcomes (Fig. 1).
There were no statistically significant intervention effects observed at the 3-month assessment. Similarly, no statistically significant effects were observed when the intervention effects were pooled across both time points. However, the 12-month intervention effects showed a significantly improved overall cardiometabolic risk score for intervention participants relative to controls (−0.11, 95% CI, −0.29 to −0.00; P = 0.046). The direction of the effect for all biomarkers contributing to the clustered metabolic risk score favored the intervention, with the effect for fasting glucose reaching statistical significance (−0.34 mmol·L−1; 95% CI, −0.65 to −0.03; P = 0.028) relative to controls. The CI for the intervention effect for all biomarkers (with the exception of body weight) included potentially meaningful effects (i.e., included the MDI), with the direction of the effects mainly (although not always) favoring the intervention.
When examining the within group changes (Table 2), the intervention effects on cardiometabolic risk and fasting glucose appeared to occur due to long-term worsening within the control group (0.14, 95% CI, 0.03–0.25; P = 0.023; and 0.41 mmol·L−1; 95% CI, 0.16–0.66; P < 0.001, respectively) that exceeded any intervention group change. None of the biomarker changes within the intervention group were statistically significant (Table 2). The completer’s analyses results strongly resembled those presented from the multiply imputed data (see Table, Supplemental Digital Content 3, Changes from baseline in glucose metabolism, and differences between intervention and control groups, adjusting for confounders [completers analysis], https://links.lww.com/MSS/A955; see Table Supplemental Digital Content 4, Changes from baseline in body composition, lipids, and blood pressure, and differences between intervention and control groups, adjusting for confounders [completers analysis], https://links.lww.com/MSS/A956).
This study evaluated the impact on the secondary cardiometabolic biomarker outcomes of Stand Up Victoria—a multicomponent workplace-delivered intervention that had produced large and significant reductions in sitting time at the workplace and overall, primarily by increasing standing (14). No significant intervention effects on biomarkers were seen at the 3-month evaluation. In contrast, at the 12-month evaluation, significant intervention effects (favoring the intervention group) were seen in fasting glucose and the clustered cardiometabolic risk score (of which fasting glucose was a component). With respect to the direction of change from the baseline values, most of the 13 other biomarker outcomes also favored the intervention group. However, these effects were not statistically significant and were mostly estimated with wide CI. These findings add to the limited evidence on the benefits of field-based sedentary behavior reduction interventions and their impact on risk biomarkers, and provide novel evidence that long-term reductions in sitting may have some benefit for indicators of cardiometabolic health in office workers.
Previous findings concerning the impact of sitting-reduction interventions on cardiometabolic risk biomarkers have been mixed. These have sometimes indicated benefits to various outcomes, including HDL cholesterol (2) and total cholesterol (11), waist circumference (1), percentage body fat, and fat-free mass (3), and insulin and HOMA-IR (1), with adverse effects seldom reported (27). In the current study, we observed a significant intervention effect for fasting glucose at 12 months (−0.34 mmol·L−1, 95% CI, −0.65 to −0.03) which was similar to the effect previously reported within our initial 4-wk pilot study (−0.30 mmol·L−1; 95% CI, −0.78 to 0.18) (13). The significant benefits to HDL cholesterol (2) and total cholesterol (11) seen in other interventions that have predominantly replaced sitting with standing were not replicated. However, collectively, findings were most consistent with the intervention having small effects in the same direction across many (but not all) biomarkers that consequently contributed to reduced overall clustered metabolic risk score. Notably, with the exception of body weight, the CI around all of the nonsignificant intervention effects included potentially meaningful effects (i.e., ≥ the MDI), consistent with the study being underpowered for these secondary biomarker outcomes. Meaningful effects may have been missed. Given that both this current study and previous studies (25,27) have been underpowered, larger interventions and/or meta-analyses may provide firmer conclusions regarding the other risk biomarkers.
Of particular note is that this study examined a general population of office workers who were not specifically selected as having a preexisting medical condition. A recent review (4) highlighted that the glycemic benefits of regularly breaking up sitting time appear to be stronger in populations with an existing medical condition (such as diabetes where elevated biomarker levels have the most room to improve) compared with a healthy population. The potential effects of an intervention such as Stand Up Victoria in chronic disease populations should be investigated. In the current study, the intervention seemed to produce benefits mostly by mitigating natural worsening in cardiometabolic risk biomarkers over time. Given the rapid escalation in global rates of diabetes and obesity (38), and the associated health and economic impacts of this (38), preventing or delaying age-related worsening (26) potentially has important implications for public health.
When there was no significant intervention effect, there was also little evidence (P ≥ 0.1) that short- and long-term intervention effects differed. However, intervention effects for glucose did differ over time, only becoming evident at 12 months. In predominantly healthy groups, it is possible that replacing sitting primarily with standing may require extended intervention durations (>12 months) to elicit any noticeable impact on risk biomarkers, especially if this benefit comes in the form of preventing or minimizing adverse changes. Notably, the intervention was designed to suit such sustainable long-term changes (29). After the three-month initial phase, there was no further input by the research team, with the ongoing intervention consisting only of the workstations and any impact on practices that had become embedded in the work teams culture and the workers’ daily activities. In recognition of the importance of these workplace-led efforts to achieving sustainable long-term change (and potentially associated population-wide impact), the intervention is now being translated to facilitate wide-scale dissemination and evaluation (15).
Several reasons are likely to have contributed to the small size of the intervention effects and absence of larger and significant effects on some of the biomarker outcomes. Compensation was not a likely explanation because there was still a substantial net sitting reduction overall (i.e., even after accounting for any potential sitting increases outside of the workplace) and the intervention did not show a large or significant adverse effect on stepping time. Nonetheless, some aspects concerning how behavior change occurred in Stand Up Victoria likely had consequences for the degree of change that did or did not eventuate in the various biomarkers. The predominant replacement of sitting with standing (as opposed to ambulation) may have been salient. The nonsignificant and small impact of the Stand Up Victoria intervention on body weight (<1 kg over 12 months) stands in contrast to the significant improvements observed in studies where sitting has been replaced predominantly with ambulation (22). This is consistent with the likely importance of energy expenditure for adiposity indicators (20). It should be explored whether interventions such as Stand Up Victoria require a different type of sitting reduction (e.g., replacing sitting with ambulatory activities) to produce more substantial benefits and/or benefits to a wider range of traditional risk biomarkers. A key next step in determining how to make sitting-reduction interventions more effective in improving cardiometabolic health will be to explore relationships with biomarker changes of the various different types of activity changes that occur during sitting-reduction interventions.
Although not explored in the current analyses, how consistently versus how sporadically the changes were made (throughout the day and across the week) may have also been relevant. Similarly, how those changes aligned with metabolically important timeframes, such as timing relative to meal intake, could have been of importance to understanding the intervention effects. However, assessments were only done in the fasted state, and any potential impact on postprandial metabolism was not captured. Adults spend much of their day in a postprandial state, including some of their time at work. Evidence from recent laboratory trials demonstrates that frequent interruptions to prolonged sitting time yields acute improvements in postprandial glucose metabolism (6,19,35). Future intervention studies should also consider potential long term impacts of sitting reduction on postprandial metabolism.
Key strengths of the study included good participant retention and the examination of long-term effects. A key limitation was that the trial was underpowered for some of the biomarker outcomes. The study design included a control group and adjusted for a range of characteristics; however, residual confounding is still possible from characteristics that were unmeasured or measured with error (e.g., dietary intake). There are some limits to generalizability as only a single organization was recruited, and participants were not randomly selected. It may be problematic that out of the individual biomarkers, the most promising findings were for fasting glucose and an overall cardiometabolic risk score that included glucose metabolism, as these outcomes were affected by the loss of data from a small (control) cluster. However, the data loss was in no way connected with the intervention, and the glucose findings were robust across both completers and multiple imputation analyses.
In conclusion, the main outcomes of the Stand Up Victoria intervention demonstrated that large shifts in sitting time can be achieved with a multicomponent intervention delivered in workplaces where there is strong organizational buy-in (14). Here, we have demonstrated that these activity changes (which were largely the replacement of sitting with standing) were sufficient to significantly improve fasting glucose and an overall indicator of cardiometabolic risk (net of control) in the long term, but were not sufficient to yield a meaningful reduction in body weight (or prevention of weight gain) over the 12-month duration of the intervention.
The authors acknowledge and thank all the participants of the Stand Up Victoria study, as well as other staff involved at the Australian Government’s Department of Human Services, particularly Tony Vane, Megan Evans, Sevasti Athiniotis and Valerie McRorie. The authors wish to thank the following project field staff: Glen Wiesner, Mary Sandilands, Kirsten Marks, Lisa Willenberg, Cameron Johnson, Bethany Howard, Stephanie Fletcher and Michael Wheeler. The views expressed in this article are those of the authors and not necessarily anyone in this acknowledgement list. The results of the present study do not constitute endorsement by ACSM.
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. GNH was supported by an NHMRC Career Development Fellowship (108029) and a National Heart Foundation of Australia Postdoctoral Fellowship (PH 12B 7054). EW was supported by an NHMRC Centre for Research Excellence Grant on Sitting Time and Chronic Disease Prevention—Measurement, Mechanisms and Interventions (1057608). N. O. was supported by an NHMRC program grant 569940), a NHMRC Senior Principal Research Fellowship (1003960) and the Victorian Government’s Operational Infrastructure Support Program. E. E. was supported by an NHMRC Senior Research Fellowship (511001). A. L. was supported by a center grant funding from the Victorian Health Promotion Foundation (2010-0509). MM was supported by an NHMRC Centre for Research Excellence grant in Obesity Policy and Food Systems (1041020). DWD was supported by an Australian Research Council Future Fellowship (FT100100918), an NHMRC Senior Research Fellowship (1078360), 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. Ergotron covered travel and accommodation expenses for both Dunstan and Healy. No further honoraria or imbursements were received. There are no other relationships or activities that could appear to have influenced the submitted work. None of the funders had involvement in the data analysis, data interpretation, data collection, or writing of the article. The findings are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
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