Objectives To evaluate the effectiveness of an intervention, with and without a height adjustable desk, on daily sitting time, and to investigate the relative effectiveness of the two interventions, and the effectiveness of both interventions on physical behaviours and physical, biochemical, psychological, and work related health and performance outcomes. Design Cluster three arm randomised controlled trial with follow-up at three and 12 months. Setting Local government councils in Leicester, Liverpool, and Greater Manchester, UK. Participants 78 clusters including 756 desk based employees in defined offices, departments, or teams from two councils in Leicester, three in Greater Manchester, and one in Liverpool. Interventions Clusters were randomised to one of three conditions: the SMART Work and Life (SWAL) intervention, the SWAL intervention with a height adjustable desk (SWAL plus desk), or control (usual practice). Main outcomes measures The primary outcome measure was daily sitting time, assessed by accelerometry, at 12 month follow-up. Secondary outcomes were accelerometer assessed sitting, prolonged sitting, standing and stepping time, and physical activity calculated over any valid day, work hours, workdays, and non-workdays, self-reported lifestyle behaviours, musculoskeletal problems, cardiometabolic health markers, work related health and performance, fatigue, and psychological measures. Results Mean age of participants was 44.7 years, 72.4% (n=547) were women, and 74.9% (n=566) were white. Daily sitting time at 12 months was significantly lower in the intervention groups (SWAL −22.2 min/day, 95% confidence interval −38.8 to −5.7 min/day, P=0.003; SWAL plus desk −63.7 min/day, −80.1 to −47.4 min/day, P<0.001) compared with the control group. The SWAL plus desk intervention was found to be more effective than SWAL at changing sitting time (−41.7 min/day, −56.3 to −27.0 min/day, P<0.001). Favourable differences in sitting and prolonged sitting time at three and 12 month follow-ups for both intervention groups and for standing time for the SWAL plus desk group were observed during work hours and on workdays. Both intervention groups were associated with small improvements in stress, wellbeing, and vigour, and the SWAL plus desk group was associated with improvements in pain in the lower extremity, social norms for sitting and standing at work, and support. Conclusions Both SWAL and SWAL plus desk were associated with a reduction in sitting time, although the addition of a height adjustable desk was found to be threefold more effective. Trial registration ISRCTN Registry ISRCTN11618007.

The aim of this study was to investigate the efficacy of a work-based multicomponent intervention to reduce office workers’ sitting time. Offices (n = 12; 89 workers) were randomized into an 8-week intervention (n = 48) incorporating organizational, individual, and environmental elements or control arm. Sitting time, physical activity, and cardiometabolic health were measured at baseline and after the intervention. Linear mixed modelling revealed no significant change in workplace sitting time, but changes in workplace prolonged sitting time (-39 min/shift), sit-upright transitions (7.8 per shift), and stepping time (12 min/shift) at follow-up were observed, in favor of the intervention group (P < 0.001). Results for cardiometabolic health markers were mixed. This short multicomponent workplace intervention was successful in reducing prolonged sitting and increasing physical activity in the workplace, although total sitting time was not reduced and the impact on cardiometabolic health was minimal.

Interrupting prolonged sitting with short multiple bouts of moderate-intensity physical activity (PA) can improve postprandial cardiometabolic risk markers. This study examined the effect of high and low frequency PA bouts (matched for total PA duration and energy expenditure) on postprandial cardiometabolic responses when compared with prolonged sitting. In this three-condition randomised crossover trial, 14 sedentary, inactive females (33.8±13.4 years, BMI 27.1±6.3 kg/m²) completed 3, 7.5 h conditions: 1) prolonged sitting (SIT), 2) high-frequency PA breaks (HIGH-FREQ) consisting of 15 ×2 min bouts of moderate-intensity treadmill PA every 30 min, and 3) low-frequency PA breaks (LOW-FREQ) consisting of 3 ×10 min bouts of moderate-intensity treadmill PA every 180 min. The PA bouts were performed at 65% of peak oxygen uptake. Net incremental area under the curve (iAUC) for each 7.5 h condition was calculated for glucose, insulin and triacylglycerol (TAG) concentrations. Insulin iAUC was significantly (p<0.026) lower during HIGH-FREQ (mean [95%CI]; 82.86 [55.02, 110.70] µU/mL∙7.5 h) than LOW-FREQ (116.61 [88.50, 144.73] µU/mL∙7.5 h) and SIT (119.98 [92.42, 147.53] µU/mL∙7.5 h). Glucose and TAG iAUC did not differ between conditions. Engaging in higher-frequency PA breaks may be effective in attenuating postprandial insulin responses compared with lower-frequency PA breaks and prolonged sitting.

The Verisense Step Count Algorithm facilitates generation of steps from wrist-worn accelerometers. Based on preliminary evidence suggesting a proportional bias with overestimation at low steps/day, but underestimation at high steps/day, the algorithm parameters have been revised. We aimed to establish validity of the original and revised algorithms relative to waist-worn ActiGraph step cadence. We also assessed whether step cadence was similar across accelerometer brand and wrist. Ninety-eight participants (age: 58.6±11.1 y) undertook six walks (~500 m hard path) at different speeds (cadence: 92.9±9.5–127.9±8.7 steps/min) while wearing three accelerometers on each wrist (Axivity, GENEActiv, ActiGraph) and an ActiGraph on the waist. Of these, 24 participants also undertook one run (~1000 m). Mean bias for the original algorithm was −21 to −26.1 steps/min (95% limits of agreement (LoA) ~±65 steps/min) and mean absolute percentage error (MAPE) 17–22%. This was unevenly distributed with increasing error as speed increased. Mean bias and 95%LoA were halved with the revised algorithm parameters (~-10 to −12 steps/min, 95%LoA ~30 steps/min, MAPE ~10-12%). Performance was similar across brand and wrist. The revised step algorithm provides a more valid measure of step cadence than the original, with MAPE similar to recently reported wrist-wear summary MAPE (7–11%).

This study evaluated the acute effects of interrupting prolonged sitting with an accumulated 2 h of light-intensity walking on postprandial cardiometabolic risk markers. In this randomised crossover trial, 24 participants (twelve males) aged 18–55 years took part in two, 6.5 h conditions: 1) prolonged sitting (SIT) and 2) sitting interrupted hourly with 20 min light-intensity treadmill desk walking at between 1.2–3.5 km/h⁻¹ (INT-SIT). Standardized meals were provided at 0 h and 3 h. Blood samples and blood pressure measures were taken hourly. Statistical analyses were completed using linear mixed models. Postprandial incremental area under the curve responses (mmol/L∙6.5 h) for glucose (4.52 [3.47, 5.56] and 6.66 [5.62, 7.71] for INT-SIT and SIT, respectively) and triglycerides (1.96 [0.96, 2.96] and 2.71 [1.70, 3.71] for INT-SIT and SIT, respectively) were significantly lower in INT-SIT than SIT. Mean systolic and diastolic blood pressure responses were lower by 3% and 4%, respectively, in INT-SIT than SIT (P < 0.05). There was no significant condition x sex interaction effect for any outcomes (P > 0.05). These findings suggest that interrupting sitting with an accumulated 2 h of light-intensity walking acutely improves cardiometabolic risk levels in males and females compared with prolonged sitting.

Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.