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Few studies have reported the cost and cost-effectiveness of workplace interventions to reduce sedentary time. The purpose of this study was to complete an economic evaluation of a multilevel intervention to reduce sitting time and increase light-intensity physical activity (LPA) among employees.We conducted a retrospective within-trial cost and cost-effectiveness analysis (CEA) to compare a 12-month multilevel intervention with (STAND+) and without (MOVE+) a sit-stand workstation, across 24 worksites (N=630 employee participants) enrolled in a cluster randomized clinical trial. We estimated the intervention costs using activity-based costing strategy. The intervention costs were further expressed as per person and per worksite. CEA was conducted using an incremental cost-effectiveness ratio (ICER) metric, expressed as costs for additional unit of sitting time (minute/day), LPA (minutes/day), cardiometabolic risk score, and quality-adjusted life years (QALY) increased/decreased at 12 months. We assessed the cost analysis and CEA from the organizational (ie, employer) perspective with a one-year time horizon.Total intervention costs were $134 and $72 per person, and $3939 and $1650 per worksite for the STAND+ (N worksites = 12; N employees = 354) and MOVE+ (N worksites = 12; N employees = 276) interventions, respectively. The ICER was $1 (95% CI $0.8-1.4) for each additional minute reduction of workplace sitting time (standardized to 8-hour workday); and $4656 per QALY gained at 12 months. There was a modest and non-significant change of loss of work productivity improvement (-0.03 hours, 95% CI -4.16-4.09 hours), which was associated with a $0.34 return for every $1 invested.The multi-level intervention with sit-stand workstations has the potential to be widely implemented to reduce workplace sitting time. Future research into work productivity outcomes in terms of cost-benefits for employers is warranted.

Background Sedentary time is associated with chronic disease and premature mortality. We tested a multilevel workplace intervention with and without sit-stand workstations to reduce sedentary time and lower cardiometabolic risk. Methods Stand and Move at Work was a group (cluster) randomized trial conducted between January 2016 and December 2017 among full-time employees; ≥18 years; and in academic, industry/healthcare, and government worksites in Phoenix, Arizona and Minneapolis/St. Paul, Minnesota, USA. Eligible worksites were randomized to (a) MOVE+, a multilevel intervention targeting reduction in sedentary time and increases in light physical activity (LPA); or (b) STAND+, the MOVE+ intervention along with sit-stand workstations to allow employees to sit or stand while working. The primary endpoints were objectively-measured workplace sitting and LPA at 12 months. The secondary endpoint was a clustered cardiometabolic risk score (blood pressure, glucose, insulin, triglycerides, and HDL-cholesterol) at 12 months. Results Worksites (N = 24; academic [n = 8], industry/healthcare [n = 8], and government [n = 8] sectors) and employees (N = 630; 27 ± 8 per worksite; 45 ± 11 years of age, 74% female) were enrolled. All worksites were retained and 487 participants completed the intervention and provided data for the primary endpoint. The adjusted between arm difference in sitting at 12 months was − 59.2 (CI: − 74.6,-43.8) min per 8 h workday, favoring STAND+, and in LPA at 12 months was + 2.2 (− 0.9,5.4) min per 8 h workday. Change in the clustered metabolic risk score was small and not statistically significant, but favored STAND+. In an exploratory subgroup of 95 participants with prediabetes or diabetes, the effect sizes were larger and clinically meaningful, all favoring STAND+, including blood glucose, triglycerides, systolic blood pressure, glycated hemoglobin, LDL-cholesterol, body weight, and body fat. Conclusions Multilevel workplace interventions that include the use of sit-stand workstations are effective for large reductions in sitting time over 12 months. Among those with prediabetes or diabetes, clinical improvements in cardiometabolic risk factors and body weight may be realized. Trial registration ClinicalTrials.gov Identifier: NCT02566317. Registered 2 October 2015, first participant enrolled 11 January 2016.

Multicomponent interventions that use sit-stand workstations (SSW) to reduce workplace sitting time have been largely successful. However, the long-term maintenance (i.e., >12 months) of this behavior change is relatively unknown. We aimed to evaluate the 24-month maintenance of reductions in workplace sitting time, along with the effects of introducing the SSW after 12 months of multicomponent intervention only. We used data from Stand and Move at Work, a two-arm multi-site group randomized trial of 24 worksites (12 worksites per arm; N=630, 74% female, 45±11 years of age). Worksites in the STAND+ arm received 12 months of a multilevel (i.e., worksite policy, environmental, and individual-level strategies) intervention to increase light-intensity physical activity, along with a SSW, to increase standing time. Worksites in the MOVE+ arm (i.e., comparator) received the same multilevel intervention but were not provided with the SSW until after the intervention ended at 12 months. Sitting time was assessed for 7 consecutive days using the activPAL3c micro accelerometer at baseline, 12, and 24 months. Results are presented below in table form. In STAND+ , sitting time at 24 months, relative to baseline, was significantly reduced and similar in magnitude of change to what was observed at 12 months. In MOVE+ , sitting time at 24 months (a year after the SSW was introduced), relative to baseline, was significantly reduced but markedly smaller in magnitude of change than observed during the STAND+ active intervention period (i.e., 0 to 12 months). These results support the use of SSW coupled with a multicomponent intervention for optimal and long-term maintenance of reductions in workplace sitting time. Hierarchical regression models were adjusted for worksite clustering, age, sex, and baseline BMI.

Long-term effects of workplace sedentary behavior interventions on cardiometabolic risk are poorly understood. Stand and Move at Work was a multi-site group randomized trial in 24 worksites (N=630, 74% female, 45±11 years of age) of office-based employees in Phoenix, AZ and Minneapolis/St. Paul, MN. Worksites in the STAND+ study arm received 12 months of a multilevel (i.e., worksite policy, environmental, and individual-level strategies) intervention to increase light-intensity physical activity along with a sit-stand workstation (SSW) to increase standing time. Worksites in the MOVE+ study arm received the same multilevel intervention but were not provided the SSW until after the intervention ended at 12 months. The STAND+ arm, but not the MOVE+ arm, had significant reductions in sedentary time over the 12-month intervention. However, 12-month improvements in cardiometabolic risk factors were only observed among a subgroup with dysglycemia (prediabetes or diabetes). Here we report the long-term follow-up of these changes, 12 months after the end of the trial (24 months). Results are shown in the Table. The effects at 12 months were largely sustained at 24 months. Although limited by the small sample size in this subgroup, long-term meaningful and sustained improvement in blood glucose, triglycerides, systolic blood pressure, and weight may be observed from a multilevel workplace sedentary behavior intervention that includes sit-stand workstations.

Within the behavioral field, a plethora of conceptual frameworks and tools have been developed to improve transition from efficacy to effectiveness trials; however, they are limited in their ability to support new, iterative intervention design decision-making methodologies beyond traditional randomized controlled trial design. Emerging theories suggest that researchers should employ engineering based user-centered design (UCD) methods to support more iterative intervention design decision-making in the behavioral field. We present, an adaptation of a UCD tool used in the engineering field—the Quality Function Deployment “House of Quality” correlation matrix, to support iterative intervention design decision-making and documentation for multicomponent behavioral interventions and factorial trial designs. We provide a detailed description of the adapted tool—“House of Quality for Behavioral Science”, and a step-by-step use-case scenario to demonstrate the early identification of intervention flaws and prioritization of requirements. Four intervention design flaws were identified via the tool application. Completion of the relationship correlation matrix increased requirement ranking variance for the researcher (σ2 = 0.47 to 7.19) and participant (σ2 = 0.56 to 3.89) perspective. Requirement prioritization (ranking) was facilitated by factoring in the strength of the correlation between each perspective and corresponding importance. A correlational matrix tool such as the “House of Quality for Behavioral Science” may provide a structured, UCD approach that balances researcher and participant needs and identifies design flaws for pragmatic behavioral intervention design. This tool may support iterative design decision-making for multicomponent and factorial trial designs.

Objective Sit-stand workstations may result in significant reductions in workplace sitting. However, few studies have examined long-term maintenance under real-world conditions. The purpose of this study was to evaluate workplace sitting time, cardio-metabolic biomarkers, and work productivity during a workplace re-design which included the installation of sit-stand workstations. Design Natural experiment with appropriately matched comparison. Methods Office workers from distinct worksites in the same unit were recruited (Intervention, n = 24; Comparison, n = 12). Intervention arm participants received a sit-stand workstation and 4 months of sitting-specific motivational support. The comparison arm received 4 months of ergonomic focused motivational support. Time spent in sitting, standing, and other physical activity were measured by activPAL3c for a week. Cardio-metabolic biomarkers and work productivity were also measured. Assessments occurred at baseline, 4 months, and 18 months. Results At 4 months, work sitting time was reduced by 56.7 ± 89.1 min/8 h workday (d = −0.64), relative to comparison. Standing time (37.4 ± 69.2 min/8 h workday; d = 0.54) and sit-to-stand transitions (3.3 ± 0.4 min/8 h workday, d = 0.44) were also improved relative to comparison. At 18 months, work sitting time reductions (52.6 ± 68.3 min/8 h workday; d = −0.77) and standing time improvements (17.7 ± 54.8 min/8 h workday, d = 0.32) were maintained in the intervention group relative to comparison. Cardio-metabolic and work productivity changes were mixed; however, strongest effects favoring the intervention group were observed at 18 months. Conclusions Sit-stand workstations, accompanied with behavioral support, were effective in reducing workplace and overall daily sitting and increasing standing time in a real-world setting. The effect appears to have been sustained for 18 months, with mixed results in cardio-metabolic and productivity outcomes.

Purpose: To review enrollment strategies, participation barriers, and program reach of a large, 2-year workplace intervention targeting sedentary behavior. Approach: Cross-sectional, retrospective review. Setting: Twenty-four worksites balanced across academic, industry, and government sectors in Minneapolis/Saint Paul (Minnesota) and Phoenix (Arizona) regions. Participants: Full-time (≥30+ h/wk), sedentary office workers. Methods: Reach was calculated as the proportion of eligible employees who enrolled in the intervention ([N enrolled/(proportion of eligible employees × N total employees)] × 100). Mean (1 standard deviation) and median worksite sizes were calculated at each enrollment step. Participation barriers and modifications were recorded by the research team. A survey was sent to a subset of nonparticipants (N = 57), and thematic analyses were conducted to examine reasons for nonparticipation, positive impacts, and negative experiences. Results: Employer reach was 65% (56 worksites invited to participate; 66% eligible of 56 responses; 24 enrolled). Employee reach was 58% (1317 invited to participate, 83% eligible of 906 responses; 632 enrolled). Postrandomization, on average, 59% (15%) of the worksites participated. Eighteen modifications were developed to overcome participant-, context-, and research-related participation barriers. Conclusion: A high proportion of worksites and employees approached to participate in a sedentary behavior reduction intervention engaged in the study. Interventions that provide flexible enrollment, graded participant engagement options, and adopt a participant-centered approach may facilitate workplace intervention success.

Objectives To compare acute cognitive effects following bouts of standing (STAND), cycling (CYCLE) and walking (WALK) to a sit-only (SIT) condition. Design Randomized cross-over full-factorial study. Methods Nine overweight (BMI = 29 ± 3 kg/m 2 ) adults (30 ± 15 years; 7 females, 2 males) completed four conditions (SIT, STAND, WALK and CYCLE) across a 6 h period with a 7 days washout period between conditions. SIT consisted of uninterrupted sitting. Experimental conditions included intermittent bouts of standing (STAND), cycling (CYCLE) and walking (WALK). A cognitive performance battery (Cogstate) was completed twice in a seated position following bouts of standing and light-intensity physical activity. Mixed-effects models compared between-condition differences in standardized score (z-score), accuracy (%), and speed (log 10 ms). Results Cognitive performance z-score and accuracy measures were higher during STAND, CYCLE and WALK ( P Conclusions Compared to uninterrupted sitting, short bouts of standing or light-intensity cycling and walking may improve acute cognitive performance.

Introduction: Desk-based office workers are at occupational risk for poor health outcomes from excessive time spent sitting. Sit-stand workstations are used to mitigate sitting, but lack of workstation usage has been observed. Point-of-choice (PoC) prompts offer a complementary strategy for office workers to break up their sitting time. Study purpose: The purpose of this study was to examine the preliminary efficacy, preference, and acceptability of a theory-driven (i.e., 40 unique prompts encompassing social cognitive theory; TD-PoC) and an atheoretical basic reminder PoC prompt intervention (R-PoC) on reducing sedentary behavior in office workers with self-reported low sit-stand workstation usage (≤4 h per day). Methods: In a cross-over design, participants (N = 19, 78.9% female, 39.4 ± 10.7 years of age) completed a 5-days no-prompt control condition followed by a random and counterbalanced assignment to one of the TD-PoC and R-PoC active conditions with a 1-week washout period between. Preliminary efficacy was assessed during work hours with the activPAL micro accelerometer. Preference was assessed prior to each active condition and acceptability was assessed following each active condition via questionnaire. Results: The R-PoC prompt condition significantly decreased sitting time (b[se] = −49.0 [20.8], p = 0.03) and increased standing time (b[se] = 49.8 [19.7], p = 0.02) and displayed a significant increase in sit-stand transitions (b[se] = 2.3 [1.1], p = 0.04), relative to no-prompt control. Both the R-PoC and TD-PoC prompt conditions significantly decreased time spent in prolonged sitting bouts at b[se] = −68.1 [27.8], (p = 0.02), (b[se] = −76.7 [27.1], p = 0.008) relative to no-prompt control. Overall, the TD-PoC prompt condition displayed higher preference and acceptability ratings; however, these differences were not significant (p's > 0.05). Conclusion: While the R-PoC prompt condition was slightly more efficacious than the TD-PoC prompt condition, the TD-PoC prompt condition was rated with higher preference and acceptability scores. Large variations between participants in preference, acceptability, and intervention feedback may indicate need for tailored messaging which may facilitate sustained use in the long-term.

This chapter provides a succinct overview of sedentary behaviour correlates, theories, and interventions in youth communities (schools), adult communities (worksites), and neighbourhoods. Within each community, we identify and discuss (a) observational and experimental studies examining the correlates of sedentary behaviour; (b) demographic, psychosocial, and environmental factors that influence sedentary behaviour; and (c) intervention designs and outcomes targeting sedentary behaviour. How technological advances and media influence may impact public awareness and intervention design is discussed. We also highlight the roles and responsibilities of both research and public health organizations to promote healthy behaviours. Finally, we evaluate community-based interventions to provide recommendations and future directions. We conclude that the barriers and challenges faced at the community level for reducing sedentary behaviours may vary per community setting and type. Ultimately, multilevel strategies and collaborative practices, across multiple settings that target sedentary behaviour as an independent risk factor, are needed to improve the efficacy of community-level interventions and increase the potential for future dissemination.

Background To identify social ecological correlates of objectively measured workplace sedentary behavior. Methods Participants from 24 worksites - across academic, industrial, and government sectors - wore an activPAL-micro accelerometer for 7-days (Jan-Nov 2016). Work time was segmented using daily logs. Sedentary behavior outcomes included time spent sitting, standing, in light intensity physical activity (LPA, stepping cadence 30 min). Outcomes were standardized to an 8 h work day. Two electronic surveys were completed to derive individual (job type and work engagement), cultural (lunch away from the desk, walking at lunch and face-to-face interaction), physical (personal printer and office type) and organizational (sector) factors. Mixed-model analyses with worksite-level clustering were performed to examine multi-level associations. Secondary analyses examined job type and sector as moderators of these associations. All models were adjusted for age, race/ethnicity and gender. Results Participants (N = 478; 72% female; age: 45.0 ± 11.3 years; 77.8% non-Hispanic white) wore the activPAL-micro for 90.2 ± 15.5% of the reported workday. Walking at lunch was positively associated with LPA (5.0 ± 0.5 min/8 h, P

Purpose We evaluated the validity and sensitivity to change of a workplace questionnaire to assess sedentary behavior (SB) during and outside work. Methods Participants wore an activPAL and completed an SB questionnaire at two time points (baseline and 3-month follow-up). Ecological momentary assessments were used to assess workplace location (at desk vs. away from desk). Intraclass correlation coefficients, mean difference, root of mean square error, kappa agreement, and Bland-Altman plots assessed validity. Sensitivity to change after 3 months of intervention was assessed using the standardized effect size. Results Data from 546 participants (age = 45.1 ± 16.4 years, 24.9% males, 72.7% white) were analyzed. Intraclass correlation coefficients ranged from 0.08 to 0.23. SB was overestimated d ¯ ( 95 % CI ) [] by 47.9 (39.2, 56.6) min during work hours but underestimated for both non–work hours and nonworkdays by −38.3 (−47.4, 29.1) and −106.7 (124.0, −89.5) min, respectively. Participants slightly underestimated SB by −3.4 (−12.6, 5.7)% when at their desk but overestimated SB by 2.8 (−2.4, 8.0)% when not at their desk. The questionnaire demonstrated similar standardized effect size (>0.6) to the activPAL for sedentary and standing time. Conclusions Agreement between the questionnaire and activPAL was on par with other self-report measures. The questionnaire yielded valid estimates of at/away from desk SB and was sensitive to change.

Background American workers spend 70–80% of their time at work being sedentary. Traditional approaches to increase moderate-vigorous physical activity (MVPA) may be perceived to be harmful to productivity. Approaches that target reductions in sedentary behavior and/or increases in standing or light-intensity physical activity [LPA] may not interfere with productivity and may be more feasible to achieve through small changes accumulated throughout the workday Methods/design This group randomized trial (i.e., cluster randomized trial) will test the relative efficacy of two sedentary behavior focused interventions in 24 worksites across two states (N = 720 workers). The MOVE + intervention is a multilevel individual, social, environmental, and organizational intervention targeting increases in light-intensity physical activity in the workplace. The STAND + intervention is the MOVE + intervention with the addition of the installation and use of sit-stand workstations to reduce sedentary behavior and enhance light-intensity physical activity opportunities. Our primary outcome will be objectively-measured changes in sedentary behavior and light-intensity physical activity over 12 months, with additional process measures at 3 months and longer-term sustainability outcomes at 24 months. Our secondary outcomes will be a clustered cardiometabolic risk score (comprised of fasting glucose, insulin, triglycerides, HDL-cholesterol, and blood pressure), workplace productivity, and job satisfaction Discussion This study will determine the efficacy of a multi-level workplace intervention (including the use of a sit-stand workstation) to reduce sedentary behavior and increase LPA and concomitant impact on cardiometabolic health, workplace productivity, and satisfaction. Trial registration: ClinicalTrials.gov Identifier: NCT02566317 (date of registration: 10/1/2015).

This study aimed to compare 24-h and postprandial glucose responses to incremental intervals of standing (STAND), walking (WALK), and cycling (CYCLE) to a sit-only (SIT) condition.Nine overweight/obese (body mass index = 29 ± 3 kg·m) adults (30 ± 15 yr) participated in this randomized crossover full-factorial study, with each condition performed 1 wk apart. STAND, CYCLE, and WALK intervals increased from 10 to 30 min·h (2.5 h total) during an 8-h workday. WALK (1.0 mph) and STAND were matched for upright time, and WALK and CYCLE were matched for energy expenditure (~2 METs). Continuous interstitial glucose monitoring was performed for 24 h to include the 8-h workday (LAB), after-work evening hours (EVE), and sleep (SLEEP). Three 2-h postprandial periods were also analyzed. Linear mixed models were used to test for condition differences.Compared with SIT (5.7 ± 1.0 mmol·L), mean 24-h glucose during STAND (5.4 ± 0.9 mmol·L) and WALK (5.3 ± 0.9 mmol·L) were lower, and CYCLE (5.1 ± 1.0 mmol·L) was lower than all other conditions (all P0.001). During LAB and EVE, mean glucose was lower for STAND, WALK, and CYCLE compared with SIT (P0.001). During SLEEP, the mean glucose for CYCLE was lower than all other conditions (P0.001). Compared with SIT, cumulative 6-h postprandial mean glucose was 5%-12% lower (P0.001) during STAND, WALK, and CYCLE, and 6-h postprandial glucose integrated area under the curve was 24% lower during WALK (P0.05) and 44% lower during CYCLE (P0.001).Replacing sitting with regular intervals of standing or light-intensity activity during an 8-h workday reduces 24-h and postprandial glucose. These effects persist during evening hours, with CYCLE having the largest and most sustained effect.

The current methods for measuring swimmer performance are time consuming and require substantial manual interaction. Inevitably, such techniques are prone to error or require a large amount of post-event processing. The aim of the research outlined in this paper is to design a novel performance analysis tool to monitor and measure swimmer performance using wireless technology previously developed at Loughborough University and determine how to communicate these data effectively to the coach in real time. The overall system effectiveness is reliant upon effective data collection, storage, and analysis. Providing the functionality to design an effective system requires robust technology, appropriate structures in persistent storage, and interface design to communicate the capability set to the user. This paper is focused on the development of a graphical user interface (GUI); a type of human–machine interface (HMI), to support users within the swimming domain. The computer integrated manufacture open system architecture (CIMOSA), generally applied as a form of business process modelling, has been used to structure the development of a user-centric, real-time monitoring system that supports the needs of the coaches, sports scientists, and swimmers. A methodology that uses the CIMOSA reference architecture as the foundation has been proposed and implemented to create a user-driven GUI that accommodates variation in requirements of user types in the swimming domain to optimize the performance analysis methods.

The acceptance of novel performance analysis tools in any sporting domain is heavily dependent on the perceived effectiveness determined by the user type. The extent to which these varied needs can be captured and the flexibility within a systems design is investigated within the research presented in this paper through the application of conceptual enterprise models based upon the Computer Integrated Manufacturing Open System Architecture (CIMOSA) modelling framework. The CIMOSA cube has been used to accommodate a generic design that can be adapted into a swimming specific model, the aim being to develop Human Machine Interfaces (HMI’s) that can be simplified, scaled and reused within numerous sporting domains by formally modelling stakeholder requirements.

AB Purpose: This study aimed to compare ambulatory blood pressure (ABP) response to accumulated standing (STAND), cycling (CYCLE), and walking (WALK) to a sitting-only (SIT) day in adults. Methods: Nine overweight or obese (body mass index, 28.7 +/- 2.7 kg[middle dot]m-2) adults (30 +/- 15 yr) participated in this randomized crossover full-factorial study. Four conditions (WALK, STAND, CYCLE, and SIT) were randomly performed 1 wk apart. WALK, STAND, and CYCLE conditions consisted of progressively increasing activity time to accumulate 2.5 h during an 8-h simulated workday. WALK (1.0 mph) and STAND (0.0 mph) were completed on a treadmill placed underneath a standing-height desk. During CYCLE, participants pedaled on a Monark cycle ergometer at a cadence and energy expenditure equivalent to WALK. Participants remained seated during the SIT condition. Participants wore an ABP cuff from 0800 h until 2200 h on all conditions. Linear mixed models were used to test condition differences in systolic (SBP) and diastolic (DBP) blood pressure. Chi-square was used to detect frequency difference of BP load. Results: There was a whole-day (during and after work hours) SBP and DBP treatment effect (P < 0.01). Systolic blood pressure during STAND (132 +/- 17 mm Hg), WALK (133 +/- 17 mm Hg), and CYCLE (130 +/- 16 mm Hg) were lower compared with that during SIT (137 +/- 17 mm Hg) (all P < 0.01). CYCLE was lower than STAND (P = 0.04) and WALK (P < 0.01). For DBP, only CYCLE (69 +/- 12 mm Hg) was lower than SIT (71 +/- 13 mm Hg; P < 0.01). Compared with SIT, WALK, STAND, and CYCLE reduced SBP load by 4%, 4%, and 13%, respectively (all P < 0.01). Conclusions: Compared with sitting, accumulating 2.5 h of light-intensity physical activity or standing during an 8-h workday may reduce ABP during and after work hours.

The aim of the research outlined in this paper was to determine the feasibility of using a network of wireless inertial measurement units (IMUs) to determine velocity-time and power-time relationships in weight training. Inertial navigation system (INS) algorithms have been implemented to account for rotation during a lift. A wireless IMU has been developed by Loughborough University that allows multiple IMUs to be networked using an adapted SimpliciTI protocol. An Olympic barbell has been modified to allow non-invasive attachment of an IMU whilst ensuring bar rotation is captured. Velocity (in the inertial frame) has been determined through integration of accelerometer / gyroscope data allowing calculation of true force and power. Three-dimensional force and 2.5 dimensional video data were simultaneously captured to provide validation for the IMU-determined force and power calculations.