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The advancement of science demands people and organizations work together toward worthy goals, a need that is especially pronounced in nascent fields like physical behavior. Partnerships offer an ideal vehicle for long-term collaboration to build research capacity, develop new scientific endeavors, and nurture talent. In this commentary, we discuss the newly formed partnership between two key players of the field of physical behavior, the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) and the International Society for the Measurement of Physical Behaviour (ISMPB).

The development in research concerning sedentary behaviour has been rapid over the past two decades. This has led to the development of evidence and views that have become more advanced, diverse and, possibly, contentious. These include the effects of standing, the breaking up of prolonged sitting and the role of moderate-to-vigorous physical activity (MVPA) in the association between sedentary behaviour and health outcomes. The present aim is to report the views of experts (n = 21) brought together (one-day face-to-face meeting in 2018) to consider these issues and provide conclusions and recommendations for future work. Each topic was reviewed and presented by one expert followed by full group discussion, which was recorded, transcribed and analysed. The experts concluded that: (a). standing may bring benefits that accrue from postural shifts. Prolonged (mainly static) standing and prolonged sitting are both bad for health; (b). ‘the best posture is the next posture’. Regularly breaking up of sitting with postural shifts and movement is vital; (c). health effects of prolonged sitting are evident even after controlling for MVPA, but high levels of MVPA can attenuate the deleterious effects of prolonged sitting depending on the health outcome of interest. Expert discussion addressed measurement, messaging and future directions.

Background Differences in how shift workers accumulate physical activity (PA) while at work and in leisure time, on days when they are working at night, during the day, or on non-work days, are largely unexplored. The aim of this study was to improve understanding of physical activity patterns in two groups of shift workers, and to measure variations according to their shift schedules. Methods This pragmatic pilot study was conducted in two workplaces. Employees in Workplace 1 (n = 10) were required to drive for most of their shift. Workplace 2 was a manufacturing company where most of the employees’ (n = 30) occupational tasks were completed while standing. Use of time was assessed using the adult version of the Multimedia Activity Recall for Children and Adults (MARCA) administered by telephone interview. Three MARCA interviews were conducted with each participant, in order to capture a typical profile of a day-shift day, a night-shift day and a non-work day, using a two-day recall for each interview. Participants were asked to wear the activPAL3™ activity monitor, for 7 consecutive days. Paired and independent t-tests were used to compute significant differences between day-shift, night-shift and non-work days within and between workplaces. Results The total number of days quantified for the MARCA data was 192 days (64 day-shift, 60 night-shift and 68 non-work days). Workplace 2 participants reported more physical activity and less sedentary behaviour on day-shift and night shift days than on non-work days. Time spent in sedentary behaviour was similar on day-shift, night-shift and non-work days in Workplace 1. Workplace 1 participants were more sedentary (p = 0.003) and engaged in more light intensity PA (p = 0.031) on day-shift and night-shift workdays, than those from Workplace 2. Sleep times were lowest on day-shift days. Conclusion As the occupational tasks for participants in Workplace 2 involved physical activities, the findings

Background Most wearable devices that measure movement in workplaces cannot determine the context in which people spend time. This study examined the accuracy of Bluetooth sensing (10-second intervals) via the ActiGraph GT9X Link monitor to determine location in an office setting, using two simple, bespoke algorithms. Methods For one work day (mean±SD 6.2±1.1 hours), 30 office workers (30% men, aged 38±11 years) simultaneously wore chest-mounted cameras (video recording) and Bluetooth-enabled monitors (initialised as receivers) on the wrist and thigh. Additional monitors (initialised as beacons) were placed in the entry, kitchen, photocopy room, corridors, and the wearer’s office. Firstly, participant presence/absence at each location was predicted from the presence/absence of signals at that location (ignoring all other signals). Secondly, using the information gathered at multiple locations simultaneously, a simple heuristic model was used to predict at which location the participant was present. The Bluetooth-determined location for each algorithm was tested against the camera in terms of F-scores. Results When considering locations individually, the accuracy obtained was excellent in the office (F-score = 0.98 and 0.97 for thigh and wrist positions) but poor in other locations (F-score = 0.04 to 0.36), stemming primarily from a high false positive rate. The multi-location algorithm exhibited high accuracy for the office location (F-score = 0.97 for both wear positions). It also improved the F-scores obtained in the remaining locations, but not always to levels indicating good accuracy (e.g., F-score for photocopy room ≈0.1 in both wear positions). Conclusions The Bluetooth signalling function shows promise for determining where workers spend most of their time (i.e., their office). Placing beacons in multiple locations and using a rule-based decision model improved classification accuracy; however, for workplace locations visited infr

Objectives Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. Design Twenty-one participants (mean age = 27.6 ± 6.2) completed seven lab-based activity trials and a 24 h free-living trial (N = 16). Methods Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24 h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. Results Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24 h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI = 0.75–0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3 min/d (95% LOA = −46.0 to 25.4 min/d). Conclusions The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous

Objectives - To assess the validity of the Past-day Adults’ Sedentary Time-University (PAST-U) questionnaire, modified for a university population, compared with activPAL. Design - Participants (n = 57, age = 18–55 years, 47% female, 65% students) were recruited from the University of Queensland (students and staff). Methods - Participants answered the PAST-U questionnaire, which asked about time spent sitting or lying down for work, study, travel, television viewing, leisure-time computer use, reading, eating, socialising and other purposes, during the previous day. Times reported for these questions were summed to provide a measure of total sedentary time. Participants also wore an activPAL device for the full day prior to completing the questionnaire and recorded their wake and sleep times in an activity log. Total waking sedentary time derived from the activPAL was used as the criterion measure. Correlation (intraclass correlation coefficient, ICC) and agreement (Bland–Altman plots) between PAST-U and activPAL sedentary time were examined. Results - Participants were sedentary (activPAL-determined) for 66% of waking hours. The correlation between PAST-U and activPAL sedentary time for the whole sample was ICC = 0.64 [95% confidence interval (CI) = 0.45, 0.77]; and higher for non-students (ICC = 0.78, 95%CI 0.52, 0.91) than students (ICC = 0.59, 95%CI 0.33, 0.77). Bland-Altman plots revealed that the mean difference between the two measures was 5 min although limits of agreement were wide (95% limits of agreement: −3.9 to 4.1 h). Conclusions - The PAST-U provides an acceptable measure of sedentary time in this population, which included students and adults with high workplace sitting time.

Objectives Based on self-reported measures, sedentary time has been associated with chronic disease and mortality. This study examined the validity of the wrist-worn GENEactiv accelerometer for measuring sedentary time (i.e. sitting and lying) by posture classification, during waking hours in free living adults. Design Fifty-seven participants (age = 18–55 years 52% male) were recruited using convenience sampling from a large metropolitan Australian university. Methods Participants wore a GENEActiv accelerometer on their non-dominant wrist and an activPAL device attached to their right thigh for 24-h (00:00 to 23:59:59). Pearson's Correlation Coefficient was used to examine the convergent validity of the GENEActiv and the activPAL for estimating total sedentary time during waking hours. Agreement was illustrated using Bland and Altman plots, and intra-individual agreement for posture was assessed with the Kappa statistic. Results Estimates of average total sedentary time over 24-h were 623 (SD 103) min/day from the GENEActiv, and 626 (SD 123) min/day from the activPAL, with an Intraclass Correlation Coefficient of 0.80 (95% confidence intervals 0.68–0.88). Bland and Altman plots showed slight underestimation of mean total sedentary time for GENEActiv relative to activPAL (mean difference: −3.44 min/day), with moderate limits of agreement (−144 to 137 min/day). Mean Kappa for posture was 0.53 (SD 0.12), indicating moderate agreement for this sample at the individual level. Conclusions The estimation of sedentary time by posture classification of the wrist-worn GENEActiv accelerometer was comparable to the activPAL. The GENEActiv may provide an alternative, easy to wear device based measure for descriptive estimates of sedentary time in population samples.

BACKGROUND: Recent evidence links sedentary behaviour (or too much sitting) with poorer health outcomes; many adults accumulate the majority of their daily sitting time through occupational sitting and TV viewing. To further the development and targeting of evidence-based strategies there is a need for identification of the factors associated with higher levels of these behaviours. This study examined socio-demographic and health-related correlates of occupational sitting and of combined high levels of occupational sitting/TV viewing time amongst working adults. METHODS: Participants were attendees of the third wave (2011/12) of the Australian Diabetes, Obesity and Lifestyle (AusDiab) study who worked full-time (≥35 h/week; n = 1,235; 38 % women; mean ± SD age 53 ± 7 years). Logistic and multinomial logistic regression analyses were conducted (separately for women and men) to assess cross-sectional associations of self-reported occupational sitting time (categorised as high/low based on the median) and also the combination of occupational sitting time/TV viewing time (high/low for each outcome), with a number of potential socio-demographic and health-related correlates. RESULTS: Higher levels of occupational sitting (>6 h/day) were associated with higher household income for both genders. Lower levels of occupational sitting were associated with being older (women only); and, for men only, having a blue collar occupation, having a technical/vocational educational attainment, and undertaking more leisure-time physical activity (LTPA). Attributes associated with high levels of both occupational sitting and TV viewing time included white collar occupation (men only), lower levels of LTPA (both genders), higher BMI (men), and higher energy consumption (women). CONCLUSIONS: Higher household income (both genders) and professional/managerial occupations (men only) were correlates of high occupational sitting time, rel

The Advanced Pharmacy Practice Framework Steering Committee (now replaced by the Pharmacy Practitioner Development Committee) undertook work to develop an advanced pharmacy practice recognition model. As part of that work, and to assure clarity and consistency in the terminology it uses, the Committee collated the definitions used in literature sources consulted. Most recently, this involved a review of the meaning attributed to the terms ‘advanced’ and ‘extended’ when used in the context of describing aspects of professional practice. Both terms encompass the acquisition of additional expertise. While ‘advanced’ practice involves the acquisition of additional expertise to achieve a higher performance level, ‘extended’ practice relates specifically to scope of practice and involves the acquisition of additional expertise sufficient to provide services or perform tasks that are outside the usual scope of practice of the profession. Performance level operates independently of scope of practice but both must be elucidated to fully describe the professional practice of an individual practitioner.

Background An Advanced Pharmacy Practice Framework for Australia (the ‘APPF’) was published in October 2012. Further to the release of the APPF, the Advanced Pharmacy Practice Framework Steering Committee planned to develop an advanced practice recognition model for Australian pharmacists. Aim To gauge the perspectives of the pharmacy profession relating to advanced practice, via an online survey, in order to inform the design of the model. Method A survey was developed and administered to Australian pharmacists through SurveyMonkey . The survey content was based on findings from a review of national and international initiatives for recognition of advanced practice in pharmacy and other health disciplines, including medicine and nursing. Results The results of the survey showed that a high proportion of respondents considered they were already working at, or working towards achieving, an advanced level of practice. The responses relating to the assessment methods showed a clear preference for ‘submission of a professional portfolio’. A ‘written examination’ had a low level of support and in relation to an ‘oral examination by a panel’ there was a marked preference for a panel of multidisciplinary health professionals over a panel of pharmacists. Conclusion The survey outcomes will inform the development of an advanced pharmacy practice recognition model for Australian pharmacists, particularly in relation to the assessment methods. Survey outcomes also demonstrated that there is scope to further enhance the application of the APPF in the development and recognition of advanced practitioners, and to build greater awareness of the breadth of competencies encompassed by ‘advanced practice’.

The need to develop An Advanced Pharmacy Practice Framework for Australia (the “APPF”) was identified during the 2010 review of the competency standards for Australian pharmacists. The Advanced Pharmacy Practice Framework Steering Committee, a collaborative profession-wide committee comprised of representatives of ten pharmacy organisations, examined and adapted existing advanced practice frameworks, all of which were found to have been based on the Competency Development and Evaluation Group (CoDEG) Advanced and Consultant Level Framework (the “CoDEG Framework”) from the United Kingdom. Its competency standards were also found to align well with the Domains of the National Competency Standards Framework for Pharmacists in Australia (the “National Framework”). Adaptation of the CoDEG Framework created an APPF that is complementary to the National Framework, sufficiently flexible to customise for recognising advanced practice in any area of professional practice and has been approved by the boards/councils of all participating organisations. The primary purpose of the APPF is to assist the development of the profession to meet the changing health care needs of the community. However, it is also a valuable tool for assuring members of the public of the competence of an advanced practice pharmacist and the quality and safety of the services they deliver.

BACKGROUND Sedentary behavior is continuing to emerge as an important target for health promotion. The purpose of this study was to determine the validity of a self-report use of time recall tool, the Multimedia Activity Recall for Children and Adults (MARCA) in estimating time spent sitting/lying, compared with a device-based measure. METHODS Fifty-eight participants (48% female, [mean±standard deviation] 28±7.4 years of age, 23.9±3.05 kg/m2) wore an activPAL device for 24-h and the following day completed the MARCA. Pearson correlation coefficients (r) were used to analyse convergent validity of the adult MARCA compared with activPAL estimates of total sitting/lying time. Agreement was examined using Bland-Altman plots. RESULTS According to activPAL estimates, participants spent 10.4 hr/day [standard deviation (SD)=2.06] sitting or lying down while awake. The correlation between MARCA and activPAL estimates of total sit/lie time was r=0.77 (95% confidence interval = 0.64-0.86; p<0.001). Bland-Altman analyses revealed a mean bias of +0.59 hr/day with moderately wide limits of agreement (-2.35 hours to +3.53 hr/day). CONCLUSIONS This study found a moderate to strong agreement between the adult MARCA and the activPAL, suggesting that the MARCA is an appropriate tool for the measurement of time spent sitting or lying down in an adult population.