The benefits of participating in regular physical activity (PA) and reducing sedentary behaviour are wide-ranging, such as lowering the risk of coronary heart disease, diabetes, obesity, musculoskeletal disorders, anxiety, and depression (Church et al., 2011; Griffiths et al., 2012; Kelley et al., 2018; Rebar et al., 2015; Van Uffelen et al., 2010). Despite the well-documented health and wellbeing benefits, employees engagement in PA can also benefit their employers’ and organisations due to the potential reduction in absenteeism, increased productivity, and economic growth (Bouchard et al., 2012; Manini et al., 2015; Pereira et al., 2015; Reed et al., 2014). According to the latest figures from the Office of National Statistics (ONS, 2020), approximately 119 million working days were lost due to the sickness absence in the UK. The main reasons for absence included cough, flu, musculoskeletal problems, and mental health conditions (e.g., stress, depression, and anxiety; ONS, 2020). Previous research has established that participating in regular PA can lower the risk of all causes of morbidity (Church et al., 2011; Griffiths et al., 2012). More recently research has concluded that PA promotions strategies can reduce the absenteeism at university settings (Lopez et al., 2020). Therefore, research focused on employees PA and sedentary behaviours may improve presentism and better working environments, thus improving company growth and staff health and wellbeing.
There are 162 higher educational institutions (HEI) in the UK with over 378,000 members of staff with a range of job roles (Dooris et al., 2017). HEI play an essential role in shaping and developing citizenship and societal changes (Dooris et al., 2017). However, PA and sedentary behaviour related research focused on HEI context is considerably rare, and this could be one of the reasons for lack of understanding concerning the diverse job roles (e.g., academic, professional services and administrative staff) across university settings, with the majority of the existing research merely focused on academic staff (McEwan, 2013). Moreover, previous literature focused on HEI employees PA and sedentary lifestyle (SL) has identified this population as a homogeneous group. Thus, current research outcomes may not be applicable to the broader sector of this environment (Adlakha et al., 2015; Butler et al., 2015; Cooper & Barton, 2016). More specifically, job roles in a HEI environment can substantially differ in terms of flexibility and autonomy of working practices and the physical job demands. For instance, administration staff may be required to be present at their desk in a sedentary position with a little autonomy because of their job demands. In comparison, estate staff may be required to move around the building more frequently to execute more physical tasks. Thus, it is important to consider the HEI employees as a heterogeneous regarding gender and job roles when it comes to PA and SL.
Previous research has predominantly applied subjective (e.g., self-reported) methods and failed to evaluate the baseline PA and SL of employees prior conducting PA interventions (Bernaards et al., 2007; Davis et al., 2011). Although using the self-reported methods are easy to complete and are cost effective, they come with some limitations, such as relying on individual’s memory to recall every activity, bias of either over- or under-estimation of the actual PA and SL (Craig et al., 2003). Therefore, research suggested that objectively monitoring PA and SL can represent a more accurate representation of PA and SL behaviours (Bevier et al., 2020; Schaller et al., 2016). For instance, previous research which compared accelerometer and self-reported measures concluded that accelerometers demonstrated lower PA levels and sedentary behaviour compared to self-reported data (Cradock et al., 2004; Peterson et al., 2015). Moreover, Miyachi et al. (2015) collected PA and SL data through accelerometer for a whole week and concluded the accelerometer was the most appropriate tool for evaluating the existing PA levels and sitting times. Therefore, combining ActiGraph and IPAQ-LF to assess university employees PA levels and SL may provide an accurate insight about their PA and sedentary behaviours.
Research has suggested collecting data through the combination of methods provide a more accurate representation of the time, PA, intensity, and sedentary behaviour (Dubbert et al., 2004; Sylvia et al., 2014; Taraldsen et al., 2011; van der Ploeg et al., 2015). Therefore, previous research has recommended the use of combining tools rather than using single method (Brannen and Moss, 2012; Smith et al., 2017). Indeed, a mixed-methods approach of objectively and subjectively measuring PA and SL specifically in university employees across gender and job roles are lacking. Therefore, the primary aim of this cross-sectional study was to monitor the university employees’ PA and SL objectively across a whole week based on job role and gender. The secondary aim of this research was to attain a greater insight into the potential differences between accelerometer and IPAQ-LF tools undertaken in this study when recordings PA and SL. Drawing from the recent research by Safi et al. (2021) it was hypothesised that male employees would engage in more PA than females. It was also hypothesised that university staff such as academic would participate in more PA compared to office-based roles such as professional services and administrative staff.
Following an institutional ethical approval, a total of 64 employees (male = 33; female = 31) volunteered to participate in this study. Table 1 provides a summary of the participants per job roles and genders.
The accelerometer used in this study was the ActiGraph wGT3X-BT, which is a valid and reliable monitor for measuring PA and SL (Aggio et al., 2015; Trost and Tudor-Locke, 2014). More information about the ActiGraph wGT3X-BT and its manufacturer can be found from this link: https://actigraphcorp.com/support/activity-monitors/wgt3x-bt/. The sampling rate in this study was set to 100 hertz, as a sampling rate of <100 hertz cannot suitably sum or collect short bursts of activities (Brønd and Arvidsson, 2015; Sasaki et al., 2016).
The raw data of ActiGraph converted into counts and then epochs are quantified for identifying the intensity of activities such as light PA, MVPA, and sedentary behaviours for data analysis purposes (Hart et al., 2011; Rowlands and Stiles, 2012; Sasaki et al., 2016). The present study applied the commonly used cut-off points that defined sedentary behaviour from 0–99 counts per minute (CPM), light from 100–1951 CPM, moderate from 1952–5724 CPM, vigorous from 5725–9498 CPM, and anything higher than 9499 CPM are classified as very vigorous (Freedson et al., 1998). Previous research has applied and supported the use of Freedson et al. (1998) cut-off points for SL and PA intensity identification (Hart et al., 2011; Healy et al., 2008; Lyden et al., 2011; Sasaki et al., 2016; Sasaki et al., 2017; Sirard et al., 2011; van Berkel et al., 2013). Participants in this study were informed to wear the monitor on the wrist to aid adherence and as comparative results against other locations suggest the wrist provides more accurate representation of the PA and SL (Diaz et al., 2018; Dieu et al., 2017; Ellingson et al., 2017; Koster et al., 2016; Troiano et al., 2014).
For PA and sedentary behaviours data to be considered valid and reliable, the participants must wear the monitor for several hours each day (Sasaki et al., 2016). The criteria for wear time differs across studies, which could be due to the variables of interest. Nevertheless, most of the large-scale studies have reached a consensus that a minimum of three days of objective monitoring is required for consistent prediction of PA and sedentary behaviours (Aadland and Ylvisåker, 2015; Choi et al., 2012; Matthews et al., 2012; Park, 2017; Sasaki et al., 2017; van Berkel et al., 2013). Therefore, the minimum inclusion criteria for participants’ data to be included in this study were three days, with ten hours of wear time each day or 1800 minutes’ worth of data across the whole week. Participants were instructed that these 10 hours’ needs to be during the wakeful part of the day and at work.
After returning the ActiGraph participants were sent an online survey the IPAQ-LF to record their PA and SL subjectively as outlined in Figure 1. Previous research has applied the IPAQ-LF and reported that it is the most valid and reliable instrument for measuring PA levels and SL across a range of domains (Craig et al., 2003; Gustafson and Rhodes, 2006; Haskell et al., 2007). Furthermore, recent studies have compared the validity of the IPAQ-LF to accelerometers and demonstrated an acceptable level of reliability in measuring PA patterns in adults (Cleland et al., 2018; Hagströmer et al., 2006; Wanner et al., 2016; Wrzesińska et al., 2018).
Prior to the data analysis, participants were categorised according to gender (i.e., male and female) and job role (i.e., academics, administrative staff, and professional services). The ActiGraph data was downloaded via the ActiLife software as DAT and CSV excel for each participant and uploaded into the ‘scoring’ in ActiLife software for calculation before exporting. Similarly, responses to the IPAQ-LF’s duration time in options were converted from hours into minutes as per the IPAQ-LF guidelines.
The statistical tests were conducted using IBM SPSS Statistics version 24.0 software (IBM Corporation, Armonk, NY, USA). First, the data comparison was conducted for genders and job roles within each method, followed by a comparison between ActiGraph and IPAQ-LF. The level of significance for analysis was set to (p < 0.05), and the data were reported as mean and standard deviation (SD). Time spent engaged in ActiGraph and IPAQ-LF light PA, MVPA, and sedentary behaviours were not normally distributed between gender as assessed by Shapiro–Wilke’s test (p < 0.05). Therefore, a Mann-Whitney U test was conducted to determine differences between both tools light PA, MVPA and SL between genders. A Kruskal–Wallis H test was conducted to determine differences in ActiGraph and IPAQ-LF’s light PA, MVPA, and SL between job roles. The Wilcoxon Signed-Rank test was also performed to evaluate the differences between ActiGraph and IPAQ-LF light PA, MVPA, and SL across gender and job roles. The Spearman’s Rank-Order Correlation was also conducted to measure any association between the ActiGraph and IPAQ-LF light PA, MVPA, and SL.
The descriptive statistics of the mean, SD, and inferential statistical differences for total light, MVPA, and time spent sitting across genders in ActiGraph and IPAQ-LF are presented in Table 2. The ActiGraph and IPA-LF results showed no significant differences between light PA, MVPA and SL amongst genders. However, there were significant differences between light PA Z = –6.139, p = .001 and MVPA Z = –4.962, p = .001 amongst gender, but no significant differences were found in SL Z = –.869, p = .385 when both tools were compared. Moreover, there were no significant differences between light PA, MVPA, and SL between ActiGraph and IPAQ-LF amongst job roles (Table 3). However, the comparison of both tools demonstrated significant differences between job roles light PA and MVPA, but no significant differences were found in SL between job roles when both tools were compared (Table 4).
|TOTAL||COMPONENTS||MEAN (SD)||MEAN (SD)||MEAN (SD)||INFERENTIAL STATISTICS|
|ActiGraph||Light PA (mins)||1784 (1007)||954 (534)||830 (473)||U = 446.000, z = –0.880, p = 0.379|
|MVPA (mins)||1593 (1106)||856 (607)||737 (499)||U = 464.000, z = –0.638, p = 0.523|
|Sedentary time (mins)||5592 (2695)||2670 (1403)||2922 (1292)||U = 467.000, z = –0.598, p = 0.550|
|IPAQ-LF||Light PA (mins)||747 (637)||349 (311)||398 (326)||U = 582.000, z = 0.948, p = 0.343|
|MVPA (mins)||792 (754)||372 (332)||420 (422)||U = 521.000, z = 0.218, p = 0.898|
|Sedentary time (mins)||4989 (4739)||2595 (2359)||2394 (2380)||U = 569.500, z = 0.780, p = 0.436|
|ActiGraph & IPAQ-LF||Light PA (mins)||Z = –6.139, p = .001*|
|MVPA (mins)||Z = –4.962, p = .001*|
|Sedentary time (mins)||Z = –.869, p = .385|
|TOOLS||COMPONENTS||MEAN (SD)||MEAN (SD)||MEAN (SD)||INFERENTIAL STATISTICS|
|ActiGraph||Light PA (mins)||920 (467)||882 (593)||872 (480)||χ2 (2) = 0.647, p = 0.723|
|MVPA (mins)||876 (604)||692 (528)||809 (531)||χ2 (2) = 2.684, p = 0.261|
|Sedentary time (mins)||2467 (2323)||2493 (2378)||2542 (2489)||χ2 (2) = 0.258, p = 0.879|
|IPAQ-LF||Light PA (mins)||385 (379)||426 (411)||374 (351)||χ2 (2) = 0.803, p = 0.669|
|MVPA (mins)||792 (754)||372 (332)||420 (422)||χ2 (2) = 0.897, p = 0.639|
|Sedentary time (mins)||2821 (1511)||2610 (1111)||2947 (1389)||χ2 (2) = 0.439, p = 0.803|
|ACTIGRAPH & IPAQ-LF INFERENTIAL STATISTICS|
|LIGHT PA||MVPA||SEDENTARY TIME|
|Academic||Z = –4.278, p = .001*||Z = –3.404, p = .001*||Z = –.713, p = .476|
|Administration||Z = –3.342, p = .001*||Z = –2.165, p = .030*||Z = –.224, p = .823|
|Professional Services||Z = –2.736, p = .006*||Z = –2.938, p = .003*||Z = –.724, p = .469|
Spearman’s rank-order correlation analysis determined the relationship to be monotonic, from the scatterplot visually examined. There was no significant correlation between both tools light PA across employees, rs (64) = 0.442, p = 0.098. However, there was a significant weak to moderate correlation between the ActiGraph and IPAQ-LF MVPA, rs (64) = 0.321, p = 0.010. There was no significant correlation in ActiGraph and IPAQ-LF SL, rs (64) = 0.047, p = 0.711. The Wilcoxon Signed-Rank test data showed significant differences between ActiGraph and IPAQ-LF light PA, z = –6.139, p = 0.0005. There were also significant differences in ActiGraph and IPAQ-LF MVPA, z = –4.962, p = 0.0005. However, differences between sedentary behaviour were not significant in both methods, z = 0.869, p = 0.385.
The primary aimed of this cross-sectional study was to monitor the university employees’ PA and SL objectively and subjectively across a whole week based on their job roles and gender. The key findings from this study revealed that employees reported lower levels of PA and SL in the self-reported IPAQ-LF compared to the ActiGraph results between gender and job roles (p < 0.05). The lower levels of PA in the IPAQ-LF indicated that participants may not have considered reporting all activities, such as walking to a meeting, kitchen, or lecture room, as part of the PA. In comparison, the ActiGraph recorded all types of physical movements that may not have been captured by the IPAQ-LF. The additional reason for employees reporting an underestimation in the self-reported, light, and MVPA could be that IPAQ-LF did not regard activity conducted less than ten minutes; in contrast, the ActiGraph continually collected the data. Employees underestimating their time spent sitting in the IPAQ-LF compared to the ActiGraph may indicate the social desirability and recall bias (Fountaine et al., 2014). The current findings are in-line with previous research, suggesting that the self-reported methods underestimate findings because of the social desirability and individuals’ ability to recall every activity and precise time (Fountaine et al., 2014; Malik et al., 2014).
Regarding the differences between genders being significant across both tools with the ActiGraph results showing male employees engaged in a higher amount of light PA, MVPA, and spent more time being sedentary than females. While the IPAQ-LF demonstrated that female employees engaged in a greater amount of light PA but also spent higher volume of time being sedentary. This is in-line with previous research suggested that social support, such as moving together in a walking group or engaging in less intense activities, were favoured by females, whereas males were more interested in high-intensity activities (Guthold et al., 2018; Morris et al., 2019). Although employees spent most of their time being sedentary and this was consistent between genders, the findings indicated that females were spending a substantial amount of time being sedentary, which could undesirably affect their health and wellbeing. Overall, the outcome of this study in line with previous research supports the hypothesis that male were more active than females (Lindsay et al., 2016; Olney et al., 2018; Sallis et al., 2016; Safi et al., 2021). Further investigation to rationalise the comparative differences and reasons behind the discrepancies reported by males and females are required.
The significant differences between job roles shows that academic staff engaged in greater light PA and MVPA than the administration and professional services staff. This could be due to the job roles of academics as they were more likely to conduct light PA through their daily responsibilities, including walking to and from the lectures, practical sessions, and student engagement activities (Safi et al., 2021). Whereas, the administration and professional services staff are required to be presented at their desk, which may contribute to their lack of engagement in light PA and MVPA. The current findings support the hypothesis demonstrating that academic staff spent more time being active compared to professional services and administrative staff. Although academic staff spent higher volume of time engaged in light and MVPA, they still appeared to be spending a large amount of their time being sedentary which is surprising results compared to professional services and administrative staff. Spending prolonged time sedentary could reduce work productivity, negatively affect mental health, and contribute to obesity (Puig-Ribera et al., 2015; Zhu et al., 2020). Thus, it is essential to consider potential work-related interventions to reduce sedentary behaviour.
The second aim of this study was to draw comparisons between the ActiGraph and IPAQ-LF. The findings demonstrated a distinct difference between both tools. For instance, the IPAQ-LF shows under-reporting the light PA, MVPA and SL. The differences were observed between both job roles and gender. The outcome of this study differs from previous research reporting that the self-reported measure displayed an overestimation of the MVPA, and sedentary behaviour compared to the accelerometer (Cradock et al., 2004; Peterson et al., 2015). Regarding the application of which tool should be applied for measuring PA and SL, this study supports previous research regarding using an objective device for collecting detailed insight compared to the subjective methods (Aggio et al., 2015; Trost and Tudor-Locke, 2014).
The present findings demonstrated a very weak correlation between both tools when assessing light PA (r = 0.098), MVPA (r = 0.321), and sedentary behaviour (r = 0.047) amongst university employees. The outcome of this study contrast with previous studies examining the validity of IPAQ-LF against an accelerometer to assess MVPA and sedentary behaviour (r = 0.43–.56; Cleland et al., 2018). The current findings contributes to the rare literature about comparing and contrasting the ActiGraph and IPAQ-LF It further contributes to the scarce research about university employees PA and SL. Furthermore, this is one of the fewest studies to evaluate PA levels and SL of university employees concurrently through objective and subjective methods. This study contributes to the limited knowledge related to the evaluation of PA levels and sedentary behaviour of university employees across gender and job roles by applying two extensively established objective and subjective methods. The current findings further contribute to informing best practices of evaluating PA levels and SL. This practice can support the WHO global action for overcoming physical inactivity trends by 2025 and the global action plan of PA 2018-2030 by offering various combinations of measurement tools.
Despite its novel contribution, the present study is not without limitations. The advantages of ActiGraph involves collecting precise data and a high battery life of 25 days, with 4 GB memory and data storage capacity for an extensive amount of time. Although there are a range of benefits when using the ActiGraph, it may fail to identify activities individuals participated, such as cycling, swimming, or loadbearing exercise (Strath et al., 2013). Furthermore, ActiGraph does not provide information about the purpose of activities unless PA log-books, diary, or interviews are combined (Matthews et al., 2012). Moreover, the cost of ActiGraph and ActiLife software are also burdensome that may limit its viability, whereas collecting data via IPAQ-LF is easier to complete and cost-effective. However, there is a possibility of bias recall and potential for underestimating the actual data as evident in this study.
This study suggests that employees reported lower levels of light PA, MVPA, and SL in IPAQ-LF compared to ActiGraph. ActiGraph data showed that male employees were spending more time engaged in light PA, MVPA, and SL. Whereas, females reported to be spending more time engaged in light PA and SL in IPAQ-LF. Since both male and female employees were spending higher amount of time being sedentary across both tools it could be recommended that workplace PA and health interventions focused on reducing SL tailored to genders need could offer useful outcomes.
Although academics were more active than administration and professional services; they were spending more time being sedentary. Despite the PA engagement, this population appeared to spend a considerably higher volume of their weekly time sedentary. Moving forward, the management of universities must strive to reduce sitting time and encourage employees to adopt a healthy and active lifestyle by enabling their needs and providing PA and health-related interventions that can support an active and inclusive working environment. Thus, the present findings suggested PA, SL and health-related interventions may require a gender and role-specific approach. Nevertheless, the current study offers a benchmark for employees within a university based in the UK, whilst providing recommendations for future research within this particular domain. Thus, future research is needed to explore barriers university employees face concerning PA engagement both within and outside of the workplace and target potential interventions for the under-research population.
The authors have no competing interests to declare.
Aadland, E., & Ylvisåker, E. (2015). Reliability of objectively measured sedentary time and physical activity in adults. PLoS One, 10(7), e0133296. DOI: https://doi.org/10.1371/journal.pone.0133296
Adlakha, D., Hipp, A. J., Marx, C., Yang, L., Tabak, R., Dodson, E. A., & Brownson, R. C. (2015). Home and workplace-built environment supports for physical activity. Am J Prev Med, 48(1), 104–107. DOI: https://doi.org/10.1016/j.amepre.2014.08.023
Aggio, D., Smith, L., Fisher, A., & Hamer, M. (2015). Association of light exposure on physical activity and sedentary time in young people. Int J Environ Res Public Health, 12(3), 2941–2949. DOI: https://doi.org/10.3390/ijerph120302941
Bernaards, C. A., Belin, T. R., & Schafer, J. L. (2007). Robustness of a multivariate normal approximation for imputation of incomplete binary data. Statistics in medicine, 26(6), 1368–1382. DOI: https://doi.org/10.1002/sim.2619
Bevier, W., Glantz, N., Hoppe, C., Glass, J. M., Larez, A., Chen, K., & Kerr, D. (2020). Self-reported and objectively measured physical activity levels among Hispanic/Latino adults with type 2 diabetes. BMJ Open Diabetes Research and Care, 8(1), e000893. DOI: https://doi.org/10.1136/bmjdrc-2019-000893
Bouchard, C., Blair, S. N., & Haskell, W. (2012). Physical activity and health. Human Kinetics. DOI: https://doi.org/10.5040/9781492595717
Brannen, J., & Moss, G. (2012). Critical issues in designing mixed methods policy research. American Behavioral Scientist, 56(6), 789–801. DOI: https://doi.org/10.1177/0002764211433796
Brønd, J. C., & Arvidsson, D. (2015). Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts. Journal of applied physiology, 120(3), 362–369. DOI: https://doi.org/10.1152/japplphysiol.00628.2015
Butler, C. E., Clark, B. R., Burlis, T. L., Castillo, J. C., & Racette, S. B. (2015). Physical activity for campus employees: a university worksite wellness program. Journal of Physical Activity and Health, 12(4), 470–476. DOI: https://doi.org/10.1123/jpah.2013-0185
Choi, L., Ward, S. C., Schnelle, J. F., & Buchowski, M. S. (2012). Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc, 44(10), 2009. DOI: https://doi.org/10.1249/MSS.0b013e318258cb36
Church, T. S., Thomas, D. M., Tudor-Locke, C., Katzmarzyk, P. T., Earnest, C. P., Rodarte, R. Q., Martin, C. K., Blair, S. N., & Bouchard, C. (2011). Trends over 5 decades in US occupation-related physical activity and their associations with obesity. PLoS One, 6(5), e19657. DOI: https://doi.org/10.1371/journal.pone.0019657
Cleland, C., Ferguson, S., Ellis, G., & Hunter, R. F. (2018). Validity of the International Physical Activity Questionnaire (IPAQ) for assessing moderate-to-vigorous physical activity and sedentary behaviour of older adults in the United Kingdom. BMC medical research methodology, 18(1), 176. DOI: https://doi.org/10.1186/s12874-018-0642-3
Cooper, K., & Barton, G. C. (2016). An exploration of physical activity and wellbeing in university employees. Perspectives in Public Health, 136(3), 152–160. DOI: https://doi.org/10.1177/1757913915593103
Cradock, A. L., Wiecha, J. L., Peterson, K. E., Sobol, A. M., Colditz, G. A., & Gortmaker, S. L. (2004). Youth recall and TriTrac accelerometer estimates of physical activity levels. Med Sci Sports Exerc, 36(3), 525–532. DOI: https://doi.org/10.1249/01.MSS.0000117112.76067.D3
Craig, C. L., Marshall, A. L., Sjorstrom, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., Pratt, M., Ekelund, U., Yngve, A., & Sallis, J. F. (2003). International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc, 35(8), 1381–1395. DOI: https://doi.org/10.1249/01.MSS.0000078924.61453.FB
Davis, M. G., Fox, K. R., Hillsdon, M., Sharp, D. J., Coulson, J. C., & Thompson, J. L. (2011). Objectively measured physical activity in a diverse sample of older urban UK adults. Medicine & Science in Sports & Exercise, 43(4), 647–654. DOI: https://doi.org/10.1249/MSS.0b013e3181f36196
Diaz, K. M., Krupka, D. J., Chang, M. J., Kronish, I. M., Moise, N., Goldsmith, J., & Schwartz, J. E. (2018). Wrist-based cut-points for moderate-and vigorous-intensity physical activity for the Actical accelerometer in adults. Journal of sports sciences, 36(2), 206–212. DOI: https://doi.org/10.1080/02640414.2017.1293279
Dieu, O., Mikulovic, J., Fardy, P. S., Bui-Xuan, G., Béghin, L., & Vanhelst, J. (2017). Physical activity using wrist-worn accelerometers: comparison of dominant and non-dominant wrist. Clinical physiology and functional imaging, 37(5), 525–529. DOI: https://doi.org/10.1111/cpf.12337
Dooris, M., Doherty, S., & Orme, J. (2017). The application of salutogenesis in universities. In: The Handbook of Salutogenesis (pp. 237–245). Springer. DOI: https://doi.org/10.1007/978-3-319-04600-6_23
Dubbert, P. M., Vander, M. W., Kirchner, K. A., & Shaw, B. (2004). Evaluation of the 7-day physical activity recall in urban and rural men. Med Sci Sports Exerc, 36(9), 1646–1654. DOI: https://doi.org/10.1249/01.MSS.0000139893.65189.F2
Ellingson, L. D., Hibbing, P. R., Kim, Y., Frey-Law, L. A., Saint-Maurice, P. F., & Welk, G. J. (2017). Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults. Physiological measurement, 38(6), 1045. DOI: https://doi.org/10.1088/1361-6579/aa6d00
Freedson, P. S., Melanson, E., & Sirard, J. (1998). Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc, 30(5), 777–781. DOI: https://doi.org/10.1097/00005768-199805000-00021
Griffiths, K. L., Mackey, M. G., Adamson, B. J., & Pepper, K. L. (2012). Prevalence and risk factors for musculoskeletal symptoms with computer-based work across occupations. Work, 42(4), 533–541. DOI: https://doi.org/10.3233/WOR-2012-1396
Gustafson, S. L., & Rhodes, R. E. (2006). Parental correlates of physical activity in children and early adolescents. Sports Medicine, 36(1), 79–97. DOI: https://doi.org/10.2165/00007256-200636010-00006
Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2018). Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1•9 million participants. The Lancet Global Health, 6(10), e1077–e1086. DOI: https://doi.org/10.1016/S2214-109X(18)30357-7
Hagströmer, M., Oja, P., & Sjöström, M. (2006). The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public health nutrition, 9(6), 755–762. DOI: https://doi.org/10.1079/PHN2005898
Hart, T. L., McClain, J. J., & Tudor-Locke, C. (2011). Controlled and free-living evaluation of objective measures of sedentary and active behaviors. Journal of Physical Activity and Health, 8(6), 848–857. DOI: https://doi.org/10.1123/jpah.8.6.848
Haskell, W. L., Lee, I.-M., Pate, R. R., Powell, K. E., Blair, S. N., Franklin, B. A., Macera, C. A., Heath, G. W., Thompson, P. D., & Bauman, A. (2007). Physical activity and public health. Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. DOI: https://doi.org/10.1249/mss.0b013e3180616b27
Healy, G. N., Wijndaele, K., Dunstan, D. W., Shaw, J. E., Salmon, J., Zimmet, P. Z., & Owen, N. (2008). Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes care, 31(2), 369–371. DOI: https://doi.org/10.2337/dc07-1795
Kelley, G. A., Kelley, K. S., & Callahan, L. F. (2018). Community-deliverable exercise and anxiety in adults with arthritis and other rheumatic diseases: a systematic review with meta-analysis of randomised controlled trials. BMJ open, 8(2), e019138. DOI: https://doi.org/10.1136/bmjopen-2017-019138
Koster, A., Shiroma, E. J., Caserotti, P., Matthews, C. E., Chen, K. Y., Glynn, N. W., & Harris, T. B. (2016). Comparison of sedentary estimates between activPAL and hip-and wrist-worn ActiGraph. Med Sci Sports Exerc, 48(8), 1514. DOI: https://doi.org/10.1249/MSS.0000000000000924
Lindsay, D. B., Devine, S., Sealey, R. M., & Leicht, A. S. (2016). Time kinetics of physical activity, sitting, and quality of life measures within a regional workplace: a cross–sectional analysis. BMC Public Health, 16(1), 786. DOI: https://doi.org/10.1186/s12889-016-3487-x
Lyden, K., Kozey, S. L., Staudenmeyer, J. W., & Freedson, P. S. (2011). A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations. European journal of applied physiology, 111(2), 187–201. DOI: https://doi.org/10.1007/s00421-010-1639-8
Malik, S. H., Blake, H., & Suggs, L. S. (2014). A systematic review of workplace health promotion interventions for increasing physical activity. British journal of health psychology, 19(1), 149–180. DOI: https://doi.org/10.1111/bjhp.12052
Manini, T. M., Carr, L. J., King, A. C., Marshall, S., Robinson, T. N., & Rejeski, W. J. (2015). Interventions to reduce sedentary behavior. Med Sci Sports Exerc, 47(6), 1306. DOI: https://doi.org/10.1249/MSS.0000000000000519
Matthews, C. E., Hagströmer, M., Pober, D. M., & Bowles, H. R. (2012). Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc, 44(1 Suppl 1), S68. DOI: https://doi.org/10.1249/MSS.0b013e3182399e5b
Miyachi, M., Kurita, S., Tripette, J., Takahara, R., Yagi, Y., & Murakami, H. (2015). Installation of a stationary high desk in the workplace: effect of a 6-week intervention on physical activity. BMC Public Health, 15, 368. DOI: https://doi.org/10.1186/s12889-015-1724-3
Morris, S., Guell, C., & Pollard, T. M. (2019). Group walking as a “lifeline”: Understanding the place of outdoor walking groups in women’s lives. Social Science & Medicine, 238, 112489. DOI: https://doi.org/10.1016/j.socscimed.2019.112489
Office of National Statistics. (2020). https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/labourproductivity/articles/sicknessabsenceinthelabourmarket/2020. Accessed on the 14 June 2021.
Olney, N., Wertz, T., LaPorta, Z., Mora, A., Serbas, J., & Astorino, T. A. (2018). Comparison of acute physiological and psychological responses between moderate-intensity continuous exercise and three regimes of high-intensity interval training. The Journal of Strength & Conditioning Research, 32(8), 2130–2138. DOI: https://doi.org/10.1519/JSC.0000000000002154
Pereira, M. J., Coombes, B. K., Comans, T. A., & Johnston, V. (2015). The impact of onsite workplace health-enhancing physical activity interventions on worker productivity: a systematic review. Occup Environ Med, 72(6), 401–412. DOI: https://doi.org/10.1136/oemed-2014-102678
Peterson, N. E., Sirard, J. R., Kulbok, P. A., DeBoer, M. D., & Erickson, J. M. (2015). Validation of accelerometer thresholds and inclinometry for measurement of sedentary behavior in young adult university students. Research in nursing & health, 38(6), 492–499. DOI: https://doi.org/10.1002/nur.21694
Puig-Ribera, A., Martínez-Lemos, I., Giné-Garriga, M., González-Suárez, Á. M., Bort-Roig, J., Fortuño, J., Muñoz-Ortiz, L., McKenna, J., & Gilson, N. D. (2015). Self-reported sitting time and physical activity: interactive associations with mental well-being and productivity in office employees. BMC Public Health, 15(1), 72. DOI: https://doi.org/10.1186/s12889-015-1447-5
Rebar, A. L., Stanton, R., Geard, D., Short, C., Duncan, M. J., & Vandelanotte, C. (2015). A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. Health Psychology Review, 9(3), 366–378. DOI: https://doi.org/10.1080/17437199.2015.1022901
Reed, J. L., Prince, S. A., Cole, C. A., Fodor, J. G., Hiremath, S., Mullen, K.-A., Tulloch, H. E., Wright, E., & Reid, R. D. (2014). Workplace physical activity interventions and moderate-to-vigorous intensity physical activity levels among working-age women: a systematic review protocol. Systematic reviews, 3(1), 147. DOI: https://doi.org/10.1186/2046-4053-3-147
Rowlands, A., & Stiles, V. (2012). Accelerometer counts and raw acceleration output in relation to mechanical loading. Journal of Bomechanics, 45(3), 448–454. DOI: https://doi.org/10.1016/j.jbiomech.2011.12.006
Safi, A., Cole, M., Kelly, A. L., & Walker, N. C. (2021). An Evaluation of Physical Activity Levels amongst University Employees. Advances in Physical Education, 11(02), 158. DOI: https://doi.org/10.4236/ape.2021.112012
Sallis, J. F., Bull, F., Guthold, R., Heath, G. W., Inoue, S., Kelly, P., Oyeyemi, A. L., Perez, L. G., Richards, J., & Hallal, P. C. (2016). Progress in physical activity over the Olympic quadrennium. The lancet, 388(10051), 1325–1336. DOI: https://doi.org/10.1016/S0140-6736(16)30581-5
Sasaki, J. E., da Silva, K. S., da Costa, B. G. G., & John, D. (2016). Measurement of physical activity using accelerometers. In: Computer-assisted and web-based innovations in psychology, special education, and health (pp. 33–60). Elsevier. DOI: https://doi.org/10.1016/B978-0-12-802075-3.00002-4
Sasaki, J. E., Júnior, J. H., Meneguci, J., Tribess, S., Marocolo Júnior, M., Stabelini Neto, A., & Virtuoso Júnior, J. S. (2017). Number of days required for reliably estimating physical activity and sedentary behaviour from accelerometer data in older adults. Journal of sports sciences (pp. 1–6). DOI: https://doi.org/10.1080/02640414.2017.1403527
Schaller, A., Rudolf, K., Dejonghe, L., Grieben, C., & Froboese, I. (2016). Influencing factors on the overestimation of self-reported physical activity: a cross-sectional analysis of low back pain patients and healthy controls. BioMed research international, 2016. DOI: https://doi.org/10.1155/2016/1497213
Sirard, J. R., Forsyth, A., Oakes, J. M., & Schmitz, K. H. (2011). Accelerometer test-retest reliability by data processing algorithms: results from the Twin Cities Walking Study. Journal of Physical Activity and Health, 8(5), 668–674. DOI: https://doi.org/10.1123/jpah.8.5.668
Smith, B. J., Rissel, C., Shilton, T., & Bauman, A. (2017). Advancing evaluation practice in health promotion. Health Promotion Journal of Australia, 27(3), 184–186. DOI: https://doi.org/10.1071/HEv27n3_ED2
Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., Ekelund, U., Freedson, P. S., Gary, R. A., Richardson, C. R., Smith, D. T., & Swartz, A. M. (2013). Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association. Circulation, 128(20), 2259–2279. DOI: https://doi.org/10.1161/01.cir.0000435708.67487.da
Sylvia, L. G., Bernstein, E. E., Hubbard, J. L., Keating, L., & Anderson, E. J. (2014). A Practical Guide to Measuring Physical Activity. Journal of the Academy of Nutrition and Dietetics, 114(2), 199. DOI: https://doi.org/10.1016/j.jand.2013.09.018
Taraldsen, K., Askim, T., Sletvold, O., Einarsen, E. K., Grüner Bjåstad, K., Indredavik, B., & Helbostad, J. L. 2011. Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function. Physical therapy, 91(2), 277–285. DOI: https://doi.org/10.2522/ptj.20100159
Troiano, R. P., McClain, J. J., Brychta, R. J., & Chen, K. Y. (2014). Evolution of accelerometer methods for physical activity research. Br J Sports Med (pp. bjsports-2014-093546). DOI: https://doi.org/10.1136/bjsports-2014-093546
Trost, S. G., & Tudor-Locke, C. (2014). Advances in the science of objective physical activity monitoring: 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement. Br J Sports Med, 48(13), 1009–1010. DOI: https://doi.org/10.1136/bjsports-2014-093865
van Berkel, J., Proper, K. I., van Dam, A., Boot, C. R., Bongers, P. M., & van der Beek, A. J. (2013). An exploratory study of associations of physical activity with mental health and work engagement. BMC Public Health, 13(1), 558. DOI: https://doi.org/10.1186/1471-2458-13-558
van der Ploeg, H. P., Møller, S. V., Hannerz, H., van der Beek, A. J., & Holtermann, A. (2015). Temporal changes in occupational sitting time in the Danish workforce and associations with all-cause mortality: results from the Danish work environment cohort study. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 71. DOI: https://doi.org/10.1186/s12966-015-0233-1
Van Uffelen, J. G., Wong, J., Chau, J. Y., van der Ploeg, H. P., Riphagen, I., Gilson, N. D., Burton, N. W., Healy, G. N., Thorp, A. A., & Clark, B. K. (2010). Occupational sitting and health risks: a systematic review. American journal of preventive medicine, 39(4), 379–388. DOI: https://doi.org/10.1016/j.amepre.2010.05.024
Wanner, M., Probst-Hensch, N., Kriemler, S., Meier, F., Autenrieth, C., & Martin, B. W. (2016). Validation of the long international physical activity questionnaire: influence of age and language region. Preventive medicine reports, 3, 250–256. DOI: https://doi.org/10.1016/j.pmedr.2016.03.003
Wrzesińska, M., Lipert, A., Urzędowicz, B., & Pawlicki, L. (2018). Self-reported physical activity using International Physical Activity Questionnaire in adolescents and young adults with visual impairment. Disability and health journal, 11(1), 20–30. DOI: https://doi.org/10.1016/j.dhjo.2017.05.001
Zhu, X., Yoshikawa, A., Qiu, L., Lu, Z., Lee, C., & Ory, M. (2020). Healthy workplaces, active employees: A systematic literature review on impacts of workplace environments on employees’ physical activity and sedentary behavior. Building and Environment, 168, 106455. DOI: https://doi.org/10.1016/j.buildenv.2019.106455