Experiences of a walking intervention in a University community : Qualitative perspectives

Background: This study explored the experiences of university employees that participated in a walking intervention that encouraged individuals to walk more throughout their workday. The 10-week program was comprised of 5 phases (i.e. baseline, anticipating barriers, short planned walks, longer planned walks and maintenance) and utilized a pedometer diary and an online website for logging steps. The pedometer diary included “action plans” for addressing barriers and planning walking and the online dashboard provided graphical outputs that allowed participants to visualize whether they were reaching or exceeding their step targets. Methods: A subsample of 12 academic and administrative employees from the study completed open ended questionnaires at the end of the study. The questions focused on capturing the major themes of benefits/mediators and problems/moderators of the program and were assessed using phenomenological approaches. Results: Participants found a raised consciousness of physical inactivity throughout the work day. They also found it useful to have a graphical display of physical activity patterns, but found time constraints and lack of managerial support to be the primary barriers/moderators of the program. Those most likely to withdraw from the program experienced technical difficulties with objective monitors and the online website. Conclusions: Findings highlight the value in being involved in a group forum and provide insights into the challenges of supporting such programs within the workplace.

Walk@Work : an automated intervention to increase walking in university employees not achieving 10,000 daily steps

This study assessed the workday step counts of lower active (<10,000 daily steps) university employees using an automated, web-based walking intervention (Walk@Work). METHODS: Academic and administrative staff (n=390; 45.6±10.8years; BMI 27.2±5.5kg/m2; 290 women) at five campuses (Australia [x2], Canada, Northern Ireland and the United States), were given a pedometer, access to the website program (2010-11) and tasked with increasing workday walking by 1000 daily steps above baseline, every two weeks, over a six week period. Step count changes at four weeks post intervention were evaluated relative to campus and baseline walking. RESULTS: Across the sample, step counts significantly increased from baseline to post-intervention (1477 daily steps; p=0.001). Variations in increases were evident between campuses (largest difference of 870 daily steps; p=0.04) and for baseline activity status. Those least active at baseline (<5000 daily steps; n=125) increased step counts the most (1837 daily steps; p=0.001), whereas those most active (7500-9999 daily steps; n=79) increased the least (929 daily steps; p=0.001). CONCLUSIONS: Walk@Work increased workday walking by 25% in this sample overall. Increases occurred through an automated program, at campuses in different countries, and were most evident for those most in need of intervention.

Contemporaneous correlations within individuals in an automated web-based walking program (Walk@Work) over a six week period (three phases) of time

Introduction: Participants may respond to phases of a workplace walking program at different rates. This study evaluated the factors that contribute to the number of steps through phases of the program. The intervention was automated through a web-based program designed to increase workday walking. Methods: The study reviewed independent variable influences throughout phases I–III. A convenience sample of university workers (n=56; 43.6±1.7 years; BMI 27.44±.2.15 kg/m2; 48 female) were recruited at worksites in Australia. These workers were given a pedometer (Yamax SW 200) and access to the website program. For analyses, step counts entered by workers into the website were downloaded and mean workday steps were compared using a seemingly unrelated regression. This model was employed to capture the contemporaneous correlation within individuals in the study across observed time periods. Results: The model predicts that the 36 subjects with complete information took an average 7460 steps in the baseline two week period. After phase I, statistically significance increases in steps (from baseline) were explained by age, working status (full or part time), occupation (academic or professional), and self reported public transport (PT) use (marginally significant). Full time workers walked more than part time workers by about 440 steps, professionals walked about 300 steps more than academics, and PT users walked about 400 steps more than non-PT users. The ability to differentiate steps after two weeks among participants suggests a differential affect of the program after only two weeks. On average participants increased steps from week two to four by about 525 steps, but regular auto users had nearly 750 steps less than non-auto users at week four. The effect of age was diminished in the 4th week of observation and accounted for 34 steps per year of age. In phase III, discriminating between participants became more difficult, with only age effects differentiating their increase over baseline. The marginal effect of age by phase III compared to phase I, increased from 36 to 50, suggesting a 14 step per year increase from the 2nd to 6th week. Discussion: The findings suggest that participants responded to the program at different rates, with uniformity of effect achieved by the 6th week. Participants increased steps, however a tapering off occurred over time. Age played the most consistent role in predicting steps over the program. PT use was associated with increased step counts, while Auto use was associated with decreased step counts.

Excursion guidance criteria to guide control of peak emission and exposure to airborne engineered particles

The overall aim of our research was to characterize airborne particles from selected nanotechnology processes and to utilize the data to develop and test quantitative particle concentration-based criteria that can be used to trigger an assessment of particle emission controls. We investigated particle number concentration (PNC), particle mass (PM) concentration, count median diameter (CMD), alveolar deposited surface area, elemental composition, and morphology from sampling of aerosols arising from six nanotechnology processes. These included fibrous and non-fibrous particles, including carbon nanotubes (CNTs). We adopted standard occupational hygiene principles in relation to controlling peak emission and exposures, as outlined by both Safe Work Australia, (1) and the American Conference of Governmental Industrial Hygienists (ACGIH®). (2) The results from the study were used to analyses peak and 30-minute averaged particle number and mass concentration values measured during the operation of the nanotechnology processes. Analysis of peak (highest value recorded) and 30-minute averaged particle number and mass concentration values revealed: Peak PNC20–1000 nm emitted from the nanotechnology processes were up to three orders of magnitude greater than the local background particle concentration (LBPC). Peak PNC300–3000 nm was up to an order of magnitude greater, and PM2.5 concentrations up to four orders of magnitude greater. For three of these nanotechnology processes, the 30-minute average particle number and mass concentrations were also significantly different from the LBPC (p-value < 0.001). We propose emission or exposure controls may need to be implemented or modified, or further assessment of the controls be undertaken, if concentrations exceed three times the LBPC, which is also used as the local particle reference value, for more than a total of 30 minutes during a workday, and/or if a single short-term measurement exceeds five times the local particle reference value. The use of these quantitative criteria, which we are terming the universal excursion guidance criteria, will account for the typical variation in LBPC and inaccuracy of instruments, while precautionary enough to highlight peaks in particle concentration likely to be associated with particle emission from the nanotechnology process. Recommendations on when to utilize local excursion guidance criteria are also provided.

Intervening to reduce workplace sitting time : how and when do changes to sitting time occur?

Objective To investigate how and when changes in workplace sitting time occurred following a workplace intervention to inform evaluation of intervention success. Method The 4-week Stand Up Comcare study (June–September 2011) aimed to reduce workplace sitting time via regularly interrupting and replacing sitting time throughout the day. Activity monitor (activPAL3) workplace data from control (n=22) and intervention participants (n=21) were analysed. Differences in the number and usual duration of sitting bouts were used to evaluate how change occurred. To examine when change occurred, intervention effects were compared by hour since starting work and hour of the workday. Change in workplace activity (sitting, standing, stepping) was examined to further inform alignment with intervention messages. Individual variability was examined in how and when the change occurred. Results Overall, behavioural changes aligned with intervention aims. All intervention participants reduced total workplace sitting time, though there was wide individual variability observed (range −29 to −262 min per 8 h workday). On average, intervention participants reduced number of sitting bouts (−4.6 bouts (95% CI −10.1 to 1.0), p=0.106) and usual sitting bout duration (−5.6 min (95% CI −9.8 to −1.4, p=0.011)) relative to controls. Sitting time reductions were observed across the workday, though intervention effects varied by hour of the day (p=0.015). The intervention group successfully adopted the Stand Up and Sit Less intervention messages across the day. Conclusion These analyses confirmed that this workplace intervention successfully modified sitting behaviour as intended (ie, fewer and shorter sitting bouts, with changes occurring throughout the day).